diff --git a/tutorials/abide.ipynb b/tutorials/abide.ipynb
index a648f1b..388798a 100644
--- a/tutorials/abide.ipynb
+++ b/tutorials/abide.ipynb
@@ -11,19 +11,23 @@
     "tags": []
    },
    "source": [
-    "# Reducing Phenotypical Effect to Improve Multi-site Autism Classification Performance\n",
+    "# Reducing Phenotypic Effects to Improve Multi-site Autism Classification Performance\n",
     "\n",
-    "In this tutorial, we will show how to leverage patient's phenotypic information to reduce the site-dependencies of functional connectivity data using domain adaptation for improving multi-site autism classification performance.\n",
+    "In this tutorial, we demonstrate how to leverage **patient phenotypic information** to reduce **site-specific biases** in functional connectivity data using **domain adaptation**, with the goal of improving **multi-site autism classification**.\n",
     "\n",
-    "The basis of this notebook is to extend the original work by Kunda et al. from IEEE TMI 2022 that proposes a second-order functional connectivity named Tangent-Pearson, the Tangent correlation of the correlation and the application of domain adaptation for neuroimaging to reduce site-dependencies.\n",
+    "This notebook builds on the work of **Kunda et al. (IEEE TMI, 2022)**, which introduced a second-order functional connectivity representation called **Tangent-Pearson**, the tangent embedding of the Pearson correlation matrix. The original work also applied domain adaptation to reduce site dependencies in fMRI-derived features, using **site labels** as the domain information.\n",
     "\n",
-    "Kunda et al. previously applied domain adaptation by only leveraging the site labels. We will extend this work by applying domain adaptation using various phenotypic information like sex, handedness, age, and eye status.\n",
+    "We extend this approach by incorporating a **richer set** of phenotypic variables, such as sex, handedness, age, and eye status into the domain adaptation framework. This enables more effective harmonization across data collected from different imaging sites.\n",
     "\n",
-    "Our **objectives** are to:\n",
-    "1. Load the ABIDE dataset with its available preprocessing pipelines and atlasses.\n",
-    "2. Extracting functional connectivity from the time series extracted from the preprocessed scans.\n",
-    "3. Preprocess the phenotypic information to be used for domain adaptation and obtaining the classification and site labels.\n",
-    "4. Creating a pipeline to train and evaluate the performance."
+    "---\n",
+    "\n",
+    "**Objectives**\n",
+    "\n",
+    "1.\t**Load** the ABIDE dataset using different preprocessing pipelines and brain atlases.\n",
+    "2.\t**Extract** functional connectivity features from ROI-based time series.\n",
+    "3.\t**Preprocess** phenotypic variables for use in domain adaptation, and obtain class labels (ASD vs CONTROL) and site labels.\n",
+    "4.\t**Build** a training and evaluation pipeline to assess classification performance under various domain adaptation strategies.\n",
+    "5.\t**Interpret** the learned model by extracting weights for pairwise ROI feature importance and visualizing them using a connectome plot."
    ]
   },
   {
@@ -39,7 +43,29 @@
    "source": [
     "# Setup\n",
     "\n",
-    "As a starting point, we need to install some packages and have provided helper functions to assist in this tutorial."
+    "As a starting point, we will install the required packages and load a set of helper functions to assist throughout this tutorial. To keep the output clean and focused on interpretation, we will also suppress warnings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "375bb16c-a668-4582-93c2-3642ef82baf2",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "hide-input"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "os.environ[\"PYTHONWARNINGS\"] = \"ignore\""
    ]
   },
   {
@@ -55,12 +81,16 @@
    "source": [
     "## Packages\n",
     "\n",
-    "The packages that we will require for this tutorial includes PyKale and Nilearn. PyKale is an interdisciplinary machine learning library internally developed at the University of Sheffield and Nilearn is a neuroimaging library mainly intended for fMRI data analysis."
+    "The main packages required for this tutorial are PyKale and Nilearn.\n",
+    "\n",
+    "**PyKale** is an open-source interdisciplinary machine learning library developed at the University of Sheffield, with a focus on applications in biomedical and scientific domains.\n",
+    "\n",
+    "**Nilearn** is a Python library for neuroimaging analysis, widely used for processing and visualizing functional MRI (fMRI) data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "07105910",
    "metadata": {
     "editable": true,
@@ -101,7 +131,7 @@
    "source": [
     "## Helper Functions\n",
     "\n",
-    "The helper functions is adopted from the [source code](https://github.com/zaRizk7/abide-demo) originally intended for demonstrating the use cases of containers to improve reproducibility and reusability in ML experiments."
+    "The helper functions used in this tutorial are adapted from the [source code](https://github.com/zaRizk7/abide-demo), which was originally developed to demonstrate the use of containers for improving reproducibility and reusability in machine learning experiments."
    ]
   },
   {
@@ -109,20 +139,25 @@
    "id": "a6657912-3cd9-43e7-a73c-783ccbfc7442",
    "metadata": {
     "editable": true,
+    "jp-MarkdownHeadingCollapsed": true,
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
    "source": [
-    "### Feature Extractions\n",
+    "### Feature Extraction\n",
     "\n",
-    "Contains the imputation, categorical mapping, continuous standardization for the selected phenotypes and chaining functional connectivity extraction for the time series. "
+    "Includes functionality for:\n",
+    "- **Imputation** of missing phenotypic values,\n",
+    "- **Categorical encoding** of variables such as sex, handedness, and eye status,\n",
+    "- **Standardization** of continuous features like age and FIQ,\n",
+    "- **Chaining functional connectivity transformations** (e.g., Pearson, Tangent) to extract features from ROI-based time series."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "4c15c779-51c4-4942-90f4-63d0ca889a5f",
    "metadata": {
     "editable": true,
@@ -339,12 +374,14 @@
    "source": [
     "### Trainer\n",
     "\n",
-    "Contains the hyperparameter grid and a wrapper function to train the pipeline with or without domain adaptation."
+    "Defines the **hyperparameter search spac**e and provides a **wrapper function** to train the classification pipeline, with or without domain adaptation.\n",
+    "\n",
+    "Providing various search strategies (e.g., grid or randomized search) and integrates seamlessly with cross-validation and MIDA-based feature transformation when enabled."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "c7b8c1e8-263a-4692-9cec-32cfc3078a80",
    "metadata": {
     "editable": true,
@@ -395,11 +432,9 @@
     "\n",
     "MIDA_GRID = {\n",
     "    \"num_components\": [32, 64, 128, 256, None],\n",
-    "    \"kernel\": [\"linear\", \"rbf\"],\n",
     "    \"mu\": 1 / (2 * C),\n",
     "    \"eta\": 1 / (2 * C),\n",
     "    \"ignore_y\": [True, False],\n",
-    "    \"augment\": [True, False],\n",
     "}\n",
     "MIDA_GRID = {f\"domain_adapter__{key}\": value for key, value in MIDA_GRID.items()}\n",
     "\n",
@@ -533,42 +568,107 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c6c98c7e-9e0b-477e-b470-d89780867bd2",
+   "id": "36d5a108-f0a5-45d4-b517-2b48cea84e8c",
    "metadata": {
     "editable": true,
+    "jp-MarkdownHeadingCollapsed": true,
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
    "source": [
-    "# Pipeline"
+    "### Evaluation\n",
+    "\n",
+    "Contains a function to **aggregate the top-1 cross-validation scores** for each defined model, selecting the best-performing configuration per model based on a specified evaluation metric."
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "b43b0320-dffe-4233-bd34-9785770bbee6",
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5af2b149-a2c3-4dfc-bba9-038259e4fe8a",
    "metadata": {
     "editable": true,
     "slideshow": {
      "slide_type": ""
     },
-    "tags": []
+    "tags": [
+     "hide-input"
+    ]
    },
+   "outputs": [],
    "source": [
-    "## Resting-state fMRI Preprocessing\n",
+    "import pandas as pd\n",
+    "from collections import defaultdict\n",
+    "from sklearn.utils._param_validation import validate_params, StrOptions\n",
+    "\n",
+    "# Mapping for model and score display names\n",
+    "MODEL = [\"baseline\", \"site_only\", \"all_phenotypes\"]\n",
+    "MODEL = {model: \" \".join(model.split(\"_\")).title() for model in MODEL}\n",
+    "\n",
+    "SCORE = [\"accuracy\", \"precision\", \"recall\", \"f1\"]\n",
+    "SCORE = {score: score.title() for score in SCORE}\n",
+    "SCORE[\"roc_auc\"] = \"AUROC\"\n",
+    "SCORE[\"matthews_corrcoef\"] = \"MCC\"\n",
+    "\n",
+    "\n",
+    "@validate_params(\n",
+    "    {\"cv_results\": [dict], \"sort_by\": [StrOptions(set(SCORE))]},\n",
+    "    prefer_skip_nested_validation=True,\n",
+    ")\n",
+    "def compile_results(cv_results, sort_by):\n",
+    "    \"\"\"\n",
+    "    Compile and summarize cross-validation results into a formatted DataFrame.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    cv_results : dict of str -> pd.DataFrame or dict of str -> dict of str -> list\n",
+    "        Dictionary mapping model names to cross-validation results.\n",
+    "        Each entry should either be a DataFrame or a dictionary of dictionary of list.\n",
+    "    sort_by : str\n",
+    "        Metric to use for selecting the best-performing model variant.\n",
+    "        Available ones include: \"accuracy\", \"precision\", \"recall\", \"f1\", \"roc_auc\",\n",
+    "        and \"matthews_corrcoef\".\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    compiled_results : pd.DataFrame\n",
+    "        Summary table with models as rows and formatted score strings (mean ± std) as columns.\n",
+    "    \"\"\"\n",
+    "    compiled_results = defaultdict(list)\n",
     "\n",
-    "Usually, we need to preprocess the fMRI scans first before running the pipeline. However, ABIDE dataset provides several preprocessed subsets that can be downloaded directly. The ones we are going to focus on includes:\n",
-    "- `atlas`: Brain atlas used for extracting the time series. Available ones are: `\"aal\"`, `\"cc200\"`, `\"cc400\"`, `\"dosenbach160\"`, `\"ez\"`, `\"ho\"`, and `\"tt\"`. Default: `\"cc200\"`.\n",
-    "- `bp`: Band-pass filter signals between 0.01Hz and 0.1Hz. Default: `False`.\n",
-    "- `gsr`: Applies global signal regression on the signals. Default: `False`.\n",
-    "- `qc`: Only use scans that passes all quality checks. Default: `True`."
+    "    for model in cv_results:\n",
+    "        # Ensure results are in DataFrame format\n",
+    "        if not isinstance(cv_results[model], pd.DataFrame):\n",
+    "            cv_results[model] = pd.DataFrame(cv_results[model])\n",
+    "\n",
+    "        # Extract all available test scores\n",
+    "        scores = [\n",
+    "            score.replace(\"rank_test_\", \"\")\n",
+    "            for score in cv_results[model].columns\n",
+    "            if \"rank_test\" in score\n",
+    "        ]\n",
+    "\n",
+    "        # Select the best row (lowest rank) based on the given metric\n",
+    "        cv_result = cv_results[model].sort_values(f\"rank_test_{sort_by}\").iloc[0]\n",
+    "\n",
+    "        compiled_results[\"Model\"].append(MODEL[model])\n",
+    "\n",
+    "        for score in scores:\n",
+    "            mean_score = cv_result[f\"mean_test_{score}\"]\n",
+    "            std_score = cv_result[f\"std_test_{score}\"]\n",
+    "            compiled_results[SCORE[score]].append(f\"{mean_score:.4f} ± {std_score:.4f}\")\n",
+    "\n",
+    "    # Convert to DataFrame and index by model name\n",
+    "    compiled_results = pd.DataFrame(compiled_results)\n",
+    "    compiled_results = compiled_results.set_index(\"Model\")\n",
+    "\n",
+    "    return compiled_results\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "af7f3a6f-5762-4191-9e0d-f3e3eaa33003",
+   "cell_type": "markdown",
+   "id": "e35c2ebb-0811-4746-8595-6610b9f55c02",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -576,48 +676,198 @@
     },
     "tags": []
    },
-   "outputs": [],
    "source": [
-    "atlas = \"cc200\"\n",
-    "bp = False\n",
-    "gsr = False\n",
-    "qc = True"
+    "### Visualization\n",
+    "\n",
+    "Provides utility functions to:\n",
+    "- Generate **pairwise ROI labels**,\n",
+    "- Extract the **top-p most important weights** and convert them into a **symmetric matrix**,\n",
+    "- Wrap the process of **plotting connectomes** for visual interpretation of model-derived feature importances."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "bff0d762-27f6-4080-8918-38e4fb844a83",
+   "execution_count": 6,
+   "id": "21510b19-195d-4e90-9f79-7861a247e0d0",
    "metadata": {
     "editable": true,
     "slideshow": {
      "slide_type": ""
     },
-    "tags": []
+    "tags": [
+     "hide-input"
+    ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[get_dataset_dir] Dataset found in /home/zarizky/nilearn_data/ABIDE_pcp\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from nilearn.datasets import fetch_abide_pcp\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from itertools import combinations\n",
+    "from matplotlib.cm import get_cmap\n",
+    "from nilearn.plotting import plot_connectome\n",
+    "from sklearn.utils._param_validation import Interval, validate_params, Real\n",
     "\n",
-    "dataset = fetch_abide_pcp(\n",
-    "    derivatives=[f\"rois_{atlas}\"],\n",
-    "    band_pass_filtering=bp,\n",
-    "    global_signal_regression=gsr,\n",
-    "    quality_checked=qc,\n",
-    ")"
+    "\n",
+    "@validate_params(\n",
+    "    {\"rois\": [\"array-like\"], \"sep\": [str]}, prefer_skip_nested_validation=True\n",
+    ")\n",
+    "def get_pairwise_rois(rois, sep=\"---\"):\n",
+    "    \"\"\"\n",
+    "    Generate all unique ROI pair labels (upper triangle only).\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    rois : array-like of str or int\n",
+    "        List of region-of-interest (ROI) names.\n",
+    "    sep : str, optional\n",
+    "        Separator string used to join ROI pairs, by default '---'.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    pairs : np.ndarray\n",
+    "        Array of ROI pair strings in the format \"ROI1 --- ROI2\".\n",
+    "    \"\"\"\n",
+    "    pairs = [f\" {sep} \".join((a, b)) for a, b in combinations(rois, 2)]\n",
+    "    return np.array(pairs)\n",
+    "\n",
+    "\n",
+    "@validate_params(\n",
+    "    {\n",
+    "        \"weights\": [\"array-like\"],\n",
+    "        \"labels\": [\"array-like\"],\n",
+    "        \"coords\": [\"array-like\"],\n",
+    "        \"p\": [Interval(Real, 0, 1, closed=\"neither\")],\n",
+    "        \"sep\": [str],\n",
+    "    },\n",
+    "    prefer_skip_nested_validation=True,\n",
+    ")\n",
+    "def get_top_symmetric_weight(weights, labels, coords, p=0.001, sep=\"---\"):\n",
+    "    \"\"\"\n",
+    "    Construct a symmetric weight matrix for top-p ROI pairs.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    weights : array-like\n",
+    "        1D array of weights corresponding to pairwise ROI combinations.\n",
+    "    labels : array-like of str or int\n",
+    "        List of all ROI names or indices in the original data.\n",
+    "    coords : array-like\n",
+    "        Coordinates of each ROI, shape (n_rois, 3).\n",
+    "    p : float, optional\n",
+    "        Proportion of top weights to retain (default is 0.001).\n",
+    "    sep : str, optional\n",
+    "        Separator used in ROI pair labels, by default '---'.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    sym_weights : array-like\n",
+    "        Symmetric matrix of selected top weights (n_top_rois, n_top_rois).\n",
+    "    top_roi_labels : array-like\n",
+    "        Labels of ROIs included in the top-p weight pairs.\n",
+    "    top_roi_coords : array-like\n",
+    "        Coordinates corresponding to `top_roi_labels`.\n",
+    "    \"\"\"\n",
+    "    weights = pd.Series(np.copy(weights), index=get_pairwise_rois(labels))\n",
+    "    rank = weights.abs().nlargest(int(len(weights.index) * p))\n",
+    "    weights = weights[rank.index]\n",
+    "\n",
+    "    pairs = np.array([[roi.strip() for roi in col.split(sep)] for col in weights.index])\n",
+    "    unique = np.unique(pairs)\n",
+    "\n",
+    "    label_to_index = {label: idx for idx, label in enumerate(labels)}\n",
+    "    indices = [label_to_index[roi] for roi in unique]\n",
+    "\n",
+    "    top_labels = np.array(labels)[indices]\n",
+    "    top_coords = np.array(coords)[indices]\n",
+    "    mappings = {roi: idx for idx, roi in enumerate(top_labels)}\n",
+    "\n",
+    "    sym_weights = np.zeros([len(indices)] * 2)\n",
+    "    for (roi1, roi2), weight in zip(pairs, weights.values):\n",
+    "        i, j = mappings[roi1], mappings[roi2]\n",
+    "        sym_weights[i, j] = weight\n",
+    "        sym_weights[j, i] = weight\n",
+    "\n",
+    "    return sym_weights, top_labels, top_coords\n",
+    "\n",
+    "\n",
+    "@validate_params(\n",
+    "    {\n",
+    "        \"weights\": [\"array-like\"],\n",
+    "        \"labels\": [\"array-like\"],\n",
+    "        \"coords\": [\"array-like\"],\n",
+    "        \"p\": [Interval(Real, 0, 1, closed=\"neither\")],\n",
+    "        \"cmap\": [str],\n",
+    "        \"marker_size\": [Real],\n",
+    "        \"legend_params\": [dict],\n",
+    "    },\n",
+    "    prefer_skip_nested_validation=True,\n",
+    ")\n",
+    "def visualize_connectome(\n",
+    "    weights, labels, coords, p=1e-3, cmap=\"tab20\", marker_size=100, legend_params={}\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Visualize the top-p weighted ROI connections as a symmetric connectome plot.\n",
+    "\n",
+    "    This function selects the top proportion `p` of the largest (by absolute value)\n",
+    "    weights between region pairs, constructs a symmetric connectivity matrix,\n",
+    "    and plots the corresponding connectome using `nilearn.plotting.plot_connectome`.\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    weights : array-like of shape (n_edges,)\n",
+    "        Weights assigned to each unique pair of ROIs, typically from a model or analysis.\n",
+    "        These are expected to align with the order of ROI pairs generated from `labels`.\n",
+    "\n",
+    "    labels : array-like of shape (n_rois,)\n",
+    "        List of ROI (region of interest) names corresponding to the original brain atlas.\n",
+    "\n",
+    "    coords : array-like of shape (n_rois, 3)\n",
+    "        3D coordinates for each ROI, used to place nodes in the plot.\n",
+    "\n",
+    "    p : float, optional\n",
+    "        Proportion of the top-weighted connections (by absolute value) to include.\n",
+    "        Must be in the open interval (0, 1). Default is 0.001 (0.1%).\n",
+    "\n",
+    "    cmap : str, optional\n",
+    "        Matplotlib colormap name used to assign colors to ROI nodes. Default is 'tab20'.\n",
+    "\n",
+    "    marker_size : float, optional\n",
+    "        Size of ROI node markers in the connectome plot. Default is 100.\n",
+    "\n",
+    "    legend_params : dict, optional\n",
+    "        Additional keyword arguments to pass to the plot's legend (e.g., location, fontsize).\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    proj : nilearn.plotting.displays._projectors.ConnectivityProjection\n",
+    "        A `nilearn` projection object with the plotted connectome. This object supports\n",
+    "        further customization (e.g., adding markers, saving the figure).\n",
+    "\n",
+    "    Notes\n",
+    "    -----\n",
+    "    - This function assumes the weights are symmetric or that symmetry should be imposed.\n",
+    "    - Useful for visualizing model interpretability or structural/functional connectivity.\n",
+    "    \"\"\"\n",
+    "    marker_colors = get_cmap(cmap)(np.arange(len(labels)))\n",
+    "    sym_weights, labels, coords = get_top_symmetric_weight(weights, labels, coords, p)\n",
+    "    proj = plot_connectome(sym_weights, coords, colorbar=True)\n",
+    "\n",
+    "    for i in range(len(labels)):\n",
+    "        proj.add_markers(\n",
+    "            [coords[i]],\n",
+    "            marker_color=marker_colors[i],\n",
+    "            marker_size=marker_size,\n",
+    "            label=labels[i],\n",
+    "        )\n",
+    "\n",
+    "    proj.axes[next(iter(proj.axes))].ax.legend(**legend_params)\n",
+    "\n",
+    "    return proj\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "5498cc9d-2157-4372-8a55-53c39c492ed5",
+   "id": "c6c98c7e-9e0b-477e-b470-d89780867bd2",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -626,20 +876,12 @@
     "tags": []
    },
    "source": [
-    "## Phenotype Preprocessing \n",
-    "\n",
-    "The phenotypic information comes with several missing data. To utilize it for modeling, we need to impute and encode the missing values. Categorical phenotypes that we will use are `SITE_ID`, `SEX`, `HANDEDNESS_CATEGORY`, `EYE_STATUS_AT_SCAN` which will be one-hot encoded while the continuous ones including `AGE_AT_SCAN` and `FIQ` will be optionally standardized by defining the argument options:\n",
-    "- `standardize`: Standardization strategy for subject's age and FIQ. If set to `True` or `\"all\"`, standardizes the values over all subjects while `\"site\"` standardizes according to the sites. Default: `False`.\n",
-    "\n",
-    "There are several missing `HANDEDNESS_CATEGORY` values, we consider that the missing ones by default are right-handed subjects while for `FIQ`, we impute the missing values by setting them to `100`.\n",
-    "\n",
-    "The labels that assigns control and ASD group is the `DX_GROUP` phenotype that is binary encoded such that `CONTROL` and `ASD` is assigned to `0` and `1` respectively."
+    "# Pipeline"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "0996603e-3a81-4277-beab-20f189af09ed",
+   "cell_type": "markdown",
+   "id": "b43b0320-dffe-4233-bd34-9785770bbee6",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -647,30 +889,61 @@
     },
     "tags": []
    },
-   "outputs": [],
    "source": [
-    "standardize = \"site\""
+    "## Resting-state fMRI Preprocessing\n",
+    "\n",
+    "Typically, raw fMRI scans require extensive preprocessing before they can be used in a machine learning pipeline. However, the **ABIDE** dataset provides several preprocessed derivatives, which can be downloaded directly from the [Preprocessed Connectomes Project (PCP)](https://preprocessed-connectomes-project.org/abide/), eliminating the need for manual preprocessing.\n",
+    "\n",
+    "In this tutorial, we focus on the following preprocessing options:\n",
+    "- `atlas`: The **brain atlas** used to **extract ROI time series**. Available options include: `\"aal\"`, `\"cc200\"`, `\"cc400\"`, `\"dosenbach160\"`, `\"ez\"`, `\"ho\"`, and `\"tt\"`. Default: `\"aal\"`\n",
+    "- `bp`: Whether to apply **band-pass filter** to the time series between [0.01, 0.1] Hz. Default: `False`\n",
+    "- `gsr`: Whether to apply **global signal regression** to remove shared global noise from the signals. Default: `False`\n",
+    "- `qc`: Whether to include **only scans that passed all quality checks** provided by the dataset curators. Default: `True`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "2d4f160c-ed31-4f7e-9215-ba8f043576a3",
+   "id": "bff0d762-27f6-4080-8918-38e4fb844a83",
    "metadata": {
     "editable": true,
+    "scrolled": true,
     "slideshow": {
      "slide_type": ""
     },
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[get_dataset_dir] Dataset found in /home/zarizky/nilearn_data/ABIDE_pcp\n"
+     ]
+    }
+   ],
    "source": [
-    "labels, sites, phenotypes = process_phenotypic_data(dataset[\"phenotypic\"], standardize)"
+    "from nilearn.datasets import fetch_abide_pcp\n",
+    "\n",
+    "# Define preprocessing options\n",
+    "atlas = \"aal\"      # Brain atlas used to extract ROI time series (e.g., AAL atlas)\n",
+    "bp = False         # Apply band-pass filtering (0.01–0.1 Hz) to remove low- and high-frequency noise\n",
+    "gsr = False        # Do not apply global signal regression (GSR)\n",
+    "qc = True          # Include all subjects regardless of quality control status\n",
+    "\n",
+    "# Fetch the preprocessed ABIDE dataset using the specified preprocessing options\n",
+    "# This returns a dictionary containing region-wise time series and associated metadata\n",
+    "dataset = fetch_abide_pcp(\n",
+    "    derivatives=[f\"rois_{atlas}\"],     # Select the atlas-specific ROI time series (e.g., 'rois_aal')\n",
+    "    band_pass_filtering=bp,            # Whether to apply band-pass filtering\n",
+    "    global_signal_regression=gsr,      # Whether to apply global signal regression\n",
+    "    quality_checked=qc,                # Whether to include only subjects that passed QC\n",
+    ")"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c50bbd33-332b-4288-8def-beec31b83adf",
+   "id": "5498cc9d-2157-4372-8a55-53c39c492ed5",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -679,15 +952,44 @@
     "tags": []
    },
    "source": [
-    "## Feature Extraction\n",
+    "## Phenotype Preprocessing \n",
+    "\n",
+    "The phenotypic information in the dataset contains several missing values. We impute and encode it to make it suitable for modeling.\n",
+    "\n",
+    "**Categorical Variables**\n",
+    "\n",
+    "The following categorical phenotypes are used and will be **one-hot encoded**:\n",
+    "- `SITE_ID`\n",
+    "- `SEX`\n",
+    "- `HANDEDNESS_CATEGORY`\n",
+    "- `EYE_STATUS_AT_SCAN`\n",
+    "\n",
+    "**Continuous Variables**\n",
+    "\n",
+    "The following continuous phenotypes will optionally be **standardized**:\n",
+    "- `AGE_AT_SCAN`\n",
+    "- `FIQ`\n",
+    "\n",
+    "Possible options to `standardize` the continuous phenotypes includes:\n",
+    "- `\"all\"` or `True`: Standardize across all subjects\n",
+    "- `\"site\"`: Standardize within each site\n",
+    "- `False`: No standardization\n",
     "\n",
-    "- `measures`: Sequences of connectivity measure transformation to extract features from the time series. Available ones are `\"pearson\"`, `\"partial\"`, `\"tangent\"`, `\"covariance\"`, and `\"precision\"`. Default: `[\"pearson\"]`."
+    "**Handling Missing Values**\n",
+    "- `HANDEDNESS_CATEGORY`: Missing values are assumed to correspond to `right-handed` subjects.\n",
+    "- `FIQ`: Missing values are imputed with a default score of `100`.\n",
+    "\n",
+    "**Label Encoding**\n",
+    "\n",
+    "The diagnostic label `DX_GROUP` is used to assign the target class:\n",
+    "- `CONTROL` → `0`\n",
+    "- `ASD` → `1`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "35b98cf5-7e46-4ab7-a746-e76c9794a97f",
+   "id": "2d4f160c-ed31-4f7e-9215-ba8f043576a3",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -697,13 +999,24 @@
    },
    "outputs": [],
    "source": [
-    "measures = [\"pearson\"]"
+    "standardize = \"site\"  # Standardize continuous phenotypes (e.g., age, FIQ) within each site\n",
+    "\n",
+    "# Process the phenotypic metadata from the ABIDE dataset\n",
+    "# This function handles:\n",
+    "# - Imputation of missing values (e.g., assuming right-handed for missing handedness)\n",
+    "# - One-hot encoding of categorical variables (e.g., sex, site, eye status)\n",
+    "# - Standardization of continuous variables based on the chosen strategy ('site' or 'all')\n",
+    "\n",
+    "# Returns:\n",
+    "# - `labels`: Binary class labels (0 = control, 1 = ASD)\n",
+    "# - `sites`: Site identifiers for domain adaptation\n",
+    "# - `phenotypes`: Feature matrix containing encoded and standardized phenotypic variables\n",
+    "labels, sites, phenotypes = process_phenotypic_data(dataset[\"phenotypic\"], standardize)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "62495790-b83e-439f-9860-31af63da8c4f",
+   "cell_type": "markdown",
+   "id": "c50bbd33-332b-4288-8def-beec31b83adf",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -711,14 +1024,27 @@
     },
     "tags": []
    },
-   "outputs": [],
    "source": [
-    "features = extract_functional_connectivity(dataset[f\"rois_{atlas}\"], measures)"
+    "## Feature Extraction\n",
+    "\n",
+    "Functional MRI (fMRI) time series data often vary in temporal length. However, many machine learning models, including those used in this study require fixed-size input. To address this, a common approach in fMRI analysis is to compute the functional connectivity (e.g., correlation) between regions of interest (ROIs), resulting in a fixed-size feature representation.\n",
+    "\n",
+    "Specifically, we compute a connectivity matrix for each subject, and extract the upper or lower triangular part (excluding the diagonal) to obtain a feature vector suitable for model training.\n",
+    "\n",
+    "The available arguments for feature extraction are:\n",
+    "- `measures`: A sequence of connectivity transformations applied to the ROI time series. Supported options include: `\"pearson\"`, `\"partial\"`, `\"tangent\"`, `\"covariance\"`, and `\"precision\"`. Default: `[\"pearson\"]`.\n",
+    "\n",
+    "Multiple transformations can be chained to compute composite connectivity representations. For example, the **Tangent-Pearson** method proposed by *Kunda et al.* can be specified via `measures = [\"tangent\", \"pearson\"]`. This design also allows for future extensions to support higher-order connectivity features.\n",
+    "\n",
+    "```{warning}\n",
+    "Given the long runtime needed for Tangent-Pearson, we opt to use `\"pearson\"` by default.\n",
+    "```"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "a4083ba3-67bf-4dc4-88f2-22b798cb8156",
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "62495790-b83e-439f-9860-31af63da8c4f",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -726,13 +1052,21 @@
     },
     "tags": []
    },
+   "outputs": [],
    "source": [
-    "## Modeling"
+    "measures = [\"pearson\"]  # Use Pearson correlation to compute functional connectivity\n",
+    "\n",
+    "# Extract functional connectivity features from ROI time series using the specified measure\n",
+    "# - `dataset[f\"rois_{atlas}\"]`: Time series data extracted from the selected brain atlas (e.g., AAL)\n",
+    "# - `measures`: List of connectivity measures to apply; in this case, only Pearson correlation is used\n",
+    "#   to compute pairwise correlations between ROI time series, resulting in a symmetric connectivity matrix\n",
+    "\n",
+    "features = extract_functional_connectivity(dataset[f\"rois_{atlas}\"], measures)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "b1e3cbda-d9e9-4a81-81fc-49fa61c5b49e",
+   "id": "a4083ba3-67bf-4dc4-88f2-22b798cb8156",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -741,13 +1075,18 @@
     "tags": []
    },
    "source": [
-    "### Random Seed"
+    "## Modeling\n",
+    "\n",
+    "We define and train machine learning models for classifying autism spectrum disorder (ASD) using functional connectivity features.\n",
+    "\n",
+    "We explore different configurations including a baseline model, domain adaptation using site information, and an extended approach that incorporates additional phenotypic variables.\n",
+    "\n",
+    "Each model is evaluated using cross-validation, and we analyze the impact of domain adaptation on classification performance."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "b0081878-08f4-4614-a71f-486b919ea1fa",
+   "cell_type": "markdown",
+   "id": "b1e3cbda-d9e9-4a81-81fc-49fa61c5b49e",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -755,14 +1094,15 @@
     },
     "tags": []
    },
-   "outputs": [],
    "source": [
-    "seed = 0"
+    "### Random Seed\n",
+    "\n",
+    "To ensure reproducibility across runs, we define a fixed random seed. This guarantees that all operations involving randomness, such as cross-validation splits, model initialization, and hyperparameter search to produce consistent results."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "e6012573-fde9-44cd-b204-f67dbd61a9ae",
    "metadata": {
     "editable": true,
@@ -775,6 +1115,10 @@
    "source": [
     "from sklearn.utils.validation import check_random_state\n",
     "\n",
+    "seed = 0  # Set a fixed seed for reproducibility\n",
+    "\n",
+    "# Convert the seed into a numpy-compatible RandomState instance\n",
+    "# This ensures consistent behavior across scikit-learn functions that rely on randomness\n",
     "random_state = check_random_state(seed)"
    ]
   },
@@ -789,30 +1133,21 @@
     "tags": []
    },
    "source": [
-    "### Cross-Validation Split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "b2c71cfa-0222-4187-ae19-062c008ca841",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "split = \"skf\"\n",
-    "num_folds = 10\n",
-    "num_cv_repeats = 5"
+    "### Cross-Validation Split\n",
+    "\n",
+    "To evaluate model performance reliably, we define a cross-validation (CV) strategy. By default, we use **Repeated Stratified K-Fold**, which preserves class distribution across folds and supports repeated trials for more stable estimates.\n",
+    "\n",
+    "Alternatively, we can also use **Leave-P-Groups-Out (LPGO)** cross-validation. This strategy is particularly useful in multi-site studies, as it ensures that data from the same group (e.g., imaging site) are not shared between training and test sets, enabling more realistic generalization assessment under domain shift.\n",
+    "\n",
+    "For this tutorial we will specify several arguments:\n",
+    "- `split`: Defines the cross-validation strategy. `\"skf\"` for stratified k-fold to maintain label balance in each fold or use `\"lpgo\"` to evaluate generalization across sites by holding out entire groups (e.g., imaging sites). Default: `\"lpgo\"`\n",
+    "- `num_folds`: Sets how many folds to use for stratified k-fold or how many groups to leave out in LPGO. Default: `1`\n",
+    "- `num_cv_repeats`: Determines how many times the k-fold procedure is repeated to obtain more stable estimates (ignored when using LPGO). Default: `1`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "1e1e926a-e7b0-41a5-ab1f-9d35c9cad8bd",
    "metadata": {
     "editable": true,
@@ -825,14 +1160,22 @@
    "source": [
     "from sklearn.model_selection import LeavePGroupsOut, RepeatedStratifiedKFold\n",
     "\n",
+    "split = \"lpgo\"         # Cross-validation split strategy\n",
+    "num_folds = 1         # Number of folds (or groups to leave out)\n",
+    "num_cv_repeats = 1    # Number of repetitions (used only for skf)\n",
+    "\n",
+    "# Define the default cross-validation strategy:\n",
+    "# Repeated stratified k-fold maintains class distribution across folds and supports multiple repetitions\n",
     "cv = RepeatedStratifiedKFold(\n",
-    "    n_splits=num_folds,\n",
-    "    n_repeats=num_cv_repeats,\n",
-    "    random_state=random_state,\n",
+    "    n_splits=num_folds,          # Number of stratified folds\n",
+    "    n_repeats=num_cv_repeats,    # Number of repeat rounds\n",
+    "    random_state=random_state,   # Ensures reproducibility\n",
     ")\n",
     "\n",
+    "# Override with leave-p-proups-out if specified\n",
+    "# This strategy holds out `p` unique groups (e.g., sites) per fold, enabling group-level generalization\n",
     "if split == \"lpgo\":\n",
-    "    cv = LeavePGroupsOut(num_folds)"
+    "    cv = LeavePGroupsOut(num_folds)  # Use group-based CV for domain adaptation or site bias evaluation"
    ]
   },
   {
@@ -846,34 +1189,24 @@
     "tags": []
    },
    "source": [
-    "### Model Definition"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "9a84d614-69b4-4bf7-beef-a2e1f9d1cf15",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "classifier = \"logistic\"\n",
-    "mida = False\n",
-    "search_strategy = \"random\"\n",
-    "scoring = [\"accuracy\", \"roc_auc\"]\n",
-    "num_solver_iterations = 100\n",
-    "num_search_iterations = 10\n",
-    "num_jobs = None"
+    "### Model Definition\n",
+    "We define different model configurations used for classification. Each model shares the same base classifier (e.g., logistic regression), but differs in how domain adaptation is applied:\n",
+    "- **Baseline**: A standard model trained directly on functional connectivity features without domain adaptation.\n",
+    "- **Site Only**: A domain-adapted model that uses site labels as the adaptation factor, reducing site-specific bias.\n",
+    "- **All Phenotypes**: An extended domain-adapted model that incorporates multiple phenotypic variables (e.g., age, sex, handedness) to further reduce inter-site variability.\n",
+    "\n",
+    "We also specify the hyperparameter search strategy and other training parameters for each configuration, including:\n",
+    "- `classifier`: The base model to use for classification. Available options include `\"logistic\"` for logistic regression, `\"ridge\"` for ridge classifier, and `\"svm\"` for support vector machines. Default: `\"logistic\"`\n",
+    "- `scoring`: A list of performance metrics (e.g., accuracy, F1, AUROC) used during cross-validation.\n",
+    "- `num_solver_iterations`: Maximum number of iterations allowed for the solver to converge during model fitting.\n",
+    "- `num_search_iterations`: Number of hyperparameter combinations to evaluate in a randomized search.\n",
+    "- `num_jobs`: Number of CPU cores used in parallel for hyperparameter tuning and model training. Set to `-1` to use all of the available CPU cores or `-k` to use all but `k` CPU cores.\n",
+    "- `verbose`: Controls the verbosity of the training output. Higher values provide more detailed logs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "id": "654fef9c-a4cc-48f6-b626-4acfa2329291",
    "metadata": {
     "editable": true,
@@ -884,17 +1217,50 @@
    },
    "outputs": [],
    "source": [
-    "trainer = create_trainer(\n",
+    "from sklearn.base import clone\n",
+    "\n",
+    "# Define core training configuration\n",
+    "classifier = \"logistic\"                    # Classifier type (e.g., 'logistic', 'svm', etc.)\n",
+    "scoring = list(SCORE)                      # List of scoring metrics to evaluate during CV\n",
+    "num_solver_iterations = int(1e6)           # Max iterations for the solver to converge\n",
+    "num_search_iterations = 100                # Number of parameter settings sampled in randomized search\n",
+    "num_jobs = -4                              # Number of parallel jobs for training and CV\n",
+    "verbose = 1                                # Verbosity level for output\n",
+    "\n",
+    "# Initialize dictionary to store trainer instances for each model configuration\n",
+    "trainers = {}\n",
+    "\n",
+    "# Create a baseline trainer without domain adaptation (MIDA disabled)\n",
+    "trainers[\"baseline\"] = create_trainer(\n",
+    "    classifier,               # Classifier to use\n",
+    "    False,                    # Do not apply MIDA (no domain adaptation)\n",
+    "    \"grid\",                   # Use grid search for hyperparameter tuning\n",
+    "    cv,                       # Cross-validation strategy\n",
+    "    scoring,                  # Evaluation metrics\n",
+    "    num_solver_iterations,    # Max solver iterations\n",
+    "    num_search_iterations,    # Max hyperparameter trials (ignored for grid search)\n",
+    "    num_jobs,                 # Number of parallel jobs\n",
+    "    random_state,             # Random seed for reproducibility\n",
+    "    verbose,                  # Verbosity level\n",
+    ")\n",
+    "\n",
+    "# Create a trainer with MIDA enabled, using site labels as domain adaptation factors\n",
+    "trainers[\"site_only\"] = create_trainer(\n",
     "    classifier,\n",
-    "    mida,\n",
-    "    search_strategy,\n",
+    "    True,                     # Apply MIDA for domain adaptation\n",
+    "    \"random\",                 # Use randomized search for hyperparameter tuning\n",
     "    cv,\n",
     "    scoring,\n",
     "    num_solver_iterations,\n",
     "    num_search_iterations,\n",
     "    num_jobs,\n",
     "    random_state,\n",
-    ")"
+    "    verbose,\n",
+    ")\n",
+    "\n",
+    "# Clone the 'site_only' trainer to create 'all_phenotypes' trainer\n",
+    "# This enables reusing the same training configuration, while modifying only the input domain factors\n",
+    "trainers[\"all_phenotypes\"] = clone(trainers[\"site_only\"])"
    ]
   },
   {
@@ -908,12 +1274,16 @@
     "tags": []
    },
    "source": [
-    "### Training"
+    "## Training and Cross-Validation\n",
+    "\n",
+    "We train each model configuration using the previously defined cross-validation strategy. The training process involves fitting the model on functional connectivity features and evaluating its performance using multiple scoring metrics (e.g., accuracy, F1-score, AUROC).\n",
+    "\n",
+    "For models with domain adaptation, we pass additional domain factors (such as site or phenotypic variables) to guide the alignment of feature representations. Cross-validation is performed to ensure robust performance estimates and to select the best hyperparameter configuration for each model."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 13,
    "id": "c53cab98-3265-4587-9d47-abd9462398b7",
    "metadata": {
     "editable": true,
@@ -924,502 +1294,41 @@
    },
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/zarizky/miniforge3/envs/workshop-notebooks/lib/python3.10/site-packages/sklearn/model_selection/_split.py:86: UserWarning: The groups parameter is ignored by RepeatedStratifiedKFold\n",
-      "  warnings.warn(\n",
-      "/home/zarizky/miniforge3/envs/workshop-notebooks/lib/python3.10/site-packages/sklearn/model_selection/_split.py:877: UserWarning: The groups parameter is ignored by StratifiedKFold\n",
-      "  warnings.warn(\n",
-      "/home/zarizky/miniforge3/envs/workshop-notebooks/lib/python3.10/site-packages/sklearn/model_selection/_split.py:877: UserWarning: The groups parameter is ignored by StratifiedKFold\n",
-      "  warnings.warn(\n",
-      "/home/zarizky/miniforge3/envs/workshop-notebooks/lib/python3.10/site-packages/sklearn/model_selection/_split.py:877: UserWarning: The groups parameter is ignored by StratifiedKFold\n",
-      "  warnings.warn(\n",
-      "/home/zarizky/miniforge3/envs/workshop-notebooks/lib/python3.10/site-packages/sklearn/model_selection/_split.py:877: UserWarning: The groups parameter is ignored by StratifiedKFold\n",
-      "  warnings.warn(\n",
-      "/home/zarizky/miniforge3/envs/workshop-notebooks/lib/python3.10/site-packages/sklearn/model_selection/_split.py:877: UserWarning: The groups parameter is ignored by StratifiedKFold\n",
-      "  warnings.warn(\n"
+      "Fitting 20 folds for each of 31 candidates, totalling 620 fits\n"
      ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: #000;\n",
-       "  --sklearn-color-text-muted: #666;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: flex;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "  align-items: start;\n",
-       "  justify-content: space-between;\n",
-       "  gap: 0.5em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
-       "  font-size: 0.6rem;\n",
-       "  font-weight: lighter;\n",
-       "  color: var(--sklearn-color-text-muted);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 0.5em;\n",
-       "  text-align: center;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=10,\n",
-       "            random_state=RandomState(MT19937) at 0x7FA866A4DE40),\n",
-       "                   error_score=&#x27;raise&#x27;,\n",
-       "                   estimator=LogisticRegression(max_iter=1000000,\n",
-       "                                                random_state=RandomState(MT19937) at 0x7FA866A4DE40),\n",
-       "                   param_distributions={&#x27;C&#x27;: array([3.05175781e-05, 6.10351562e-05, 1.22070312e-04, 2.44140625e-04,\n",
-       "       4.88281250e-04, 9.765...\n",
-       "       1.25000000e-01, 2.50000000e-01, 5.00000000e-01, 1.00000000e+00,\n",
-       "       2.00000000e+00, 4.00000000e+00, 8.00000000e+00, 1.60000000e+01,\n",
-       "       3.20000000e+01, 6.40000000e+01, 1.28000000e+02, 2.56000000e+02,\n",
-       "       5.12000000e+02, 1.02400000e+03, 2.04800000e+03, 4.09600000e+03,\n",
-       "       8.19200000e+03, 1.63840000e+04, 3.27680000e+04])},\n",
-       "                   random_state=RandomState(MT19937) at 0x7FA866A4DE40,\n",
-       "                   refit=&#x27;accuracy&#x27;, scoring=[&#x27;accuracy&#x27;, &#x27;roc_auc&#x27;])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomizedSearchCV</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=10,\n",
-       "            random_state=RandomState(MT19937) at 0x7FA866A4DE40),\n",
-       "                   error_score=&#x27;raise&#x27;,\n",
-       "                   estimator=LogisticRegression(max_iter=1000000,\n",
-       "                                                random_state=RandomState(MT19937) at 0x7FA866A4DE40),\n",
-       "                   param_distributions={&#x27;C&#x27;: array([3.05175781e-05, 6.10351562e-05, 1.22070312e-04, 2.44140625e-04,\n",
-       "       4.88281250e-04, 9.765...\n",
-       "       1.25000000e-01, 2.50000000e-01, 5.00000000e-01, 1.00000000e+00,\n",
-       "       2.00000000e+00, 4.00000000e+00, 8.00000000e+00, 1.60000000e+01,\n",
-       "       3.20000000e+01, 6.40000000e+01, 1.28000000e+02, 2.56000000e+02,\n",
-       "       5.12000000e+02, 1.02400000e+03, 2.04800000e+03, 4.09600000e+03,\n",
-       "       8.19200000e+03, 1.63840000e+04, 3.27680000e+04])},\n",
-       "                   random_state=RandomState(MT19937) at 0x7FA866A4DE40,\n",
-       "                   refit=&#x27;accuracy&#x27;, scoring=[&#x27;accuracy&#x27;, &#x27;roc_auc&#x27;])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>best_estimator_: LogisticRegression</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.03125, max_iter=1000000,\n",
-       "                   random_state=RandomState(MT19937) at 0x7FA864732640)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=0.03125, max_iter=1000000,\n",
-       "                   random_state=RandomState(MT19937) at 0x7FA864732640)</pre></div> </div></div></div></div></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "RandomizedSearchCV(cv=RepeatedStratifiedKFold(n_repeats=5, n_splits=10,\n",
-       "            random_state=RandomState(MT19937) at 0x7FA866A4DE40),\n",
-       "                   error_score='raise',\n",
-       "                   estimator=LogisticRegression(max_iter=1000000,\n",
-       "                                                random_state=RandomState(MT19937) at 0x7FA866A4DE40),\n",
-       "                   param_distributions={'C': array([3.05175781e-05, 6.10351562e-05, 1.22070312e-04, 2.44140625e-04,\n",
-       "       4.88281250e-04, 9.765...\n",
-       "       1.25000000e-01, 2.50000000e-01, 5.00000000e-01, 1.00000000e+00,\n",
-       "       2.00000000e+00, 4.00000000e+00, 8.00000000e+00, 1.60000000e+01,\n",
-       "       3.20000000e+01, 6.40000000e+01, 1.28000000e+02, 2.56000000e+02,\n",
-       "       5.12000000e+02, 1.02400000e+03, 2.04800000e+03, 4.09600000e+03,\n",
-       "       8.19200000e+03, 1.63840000e+04, 3.27680000e+04])},\n",
-       "                   random_state=RandomState(MT19937) at 0x7FA866A4DE40,\n",
-       "                   refit='accuracy', scoring=['accuracy', 'roc_auc'])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "fit_args = {\"x\" if mida else \"X\": features, \"y\": labels, \"groups\": sites}\n",
+    "# Define common training arguments for all models: features (X), labels (y), and group info (sites)\n",
+    "fit_args = {\"X\": features, \"y\": labels, \"groups\": sites}\n",
+    "\n",
+    "# Fit the baseline model using raw features (no domain adaptation)\n",
+    "trainers[\"baseline\"].fit(**fit_args)\n",
+    "\n",
+    "# Prepare arguments for MIDA-based models by renaming X -> x (MIDA uses 'x')\n",
+    "fit_args_mida = fit_args.copy()\n",
+    "fit_args_mida[\"x\"] = fit_args_mida.pop(\"X\")\n",
     "\n",
-    "if mida and site_only:\n",
-    "    fit_args[\"factors\"] = pd.get_dummies(groups)\n",
-    "elif mida:\n",
-    "    fit_args[\"factors\"] = phenotypes\n",
+    "# Add one-hot encoded site information as domain factors for MIDA\n",
+    "fit_args_mida[\"factors\"] = pd.get_dummies(sites)\n",
     "\n",
-    "trainer.fit(**fit_args)"
+    "# Fit the 'site_only' model using domain adaptation with site labels only\n",
+    "trainers[\"site_only\"].fit(**fit_args_mida)\n",
+    "\n",
+    "# Update the MIDA input to include full phenotype metadata (e.g., age, gender, site)\n",
+    "fit_args_mida[\"factors\"] = phenotypes\n",
+    "\n",
+    "# Fit the 'all_phenotypes' model using full metadata as domain-relevant factors\n",
+    "trainers[\"all_phenotypes\"].fit(**fit_args_mida)\n",
+    "\n",
+    "# Collect cross-validation results from each trained model for later comparison\n",
+    "cv_results = {}\n",
+    "for model in trainers:\n",
+    "    # Store each model's cv_results_ (e.g., scores, ranks) in a DataFrame\n",
+    "    cv_results[model] = pd.DataFrame(trainers[model].cv_results_)"
    ]
   },
   {
@@ -1433,36 +1342,17 @@
     "tags": []
    },
    "source": [
-    "### Evaluation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "f6a1e8f1-048e-4b8f-8ad2-ab243ed872f3",
-   "metadata": {
-    "editable": true,
-    "slideshow": {
-     "slide_type": ""
-    },
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "cv_results = pd.DataFrame(trainer.cv_results_)\n",
-    "cv_results = cv_results[\n",
-    "    [f\"{aggregate}_test_{score}\" for score in scoring for aggregate in [\"mean\", \"std\"]]\n",
-    "]\n",
+    "## Evaluation\n",
+    "\n",
+    "We evaluate and compare the performance of different model configurations using cross-validation results. We aggregate the top-performing scores for each model based on a specified evaluation metric (e.g., accuracy), allowing us to assess the effectiveness of domain adaptation strategies.\n",
     "\n",
-    "cv_results = cv_results.sort_values(\"mean_test_accuracy\", ascending=False)\n",
-    "cv_results = cv_results.round(4).reset_index(drop=True)\n",
-    "cv_results.index.name = \"Rank\""
+    "By comparing models with and without domain adaptation, we can determine the impact of incorporating site and phenotypic information on multi-site autism classification performance. This analysis helps identify which configurations generalize best across heterogeneous imaging sites."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "a623e47d-0846-4286-8d7d-05a8303d96d1",
+   "execution_count": 14,
+   "id": "ccffed05-868f-40ba-8ae6-8a2661875ad7",
    "metadata": {
     "editable": true,
     "slideshow": {
@@ -1492,13 +1382,17 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>mean_test_accuracy</th>\n",
-       "      <th>std_test_accuracy</th>\n",
-       "      <th>mean_test_roc_auc</th>\n",
-       "      <th>std_test_roc_auc</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>Precision</th>\n",
+       "      <th>Recall</th>\n",
+       "      <th>F1</th>\n",
+       "      <th>AUROC</th>\n",
+       "      <th>MCC</th>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Rank</th>\n",
+       "      <th>Model</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
@@ -1507,114 +1401,201 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.6966</td>\n",
-       "      <td>0.0434</td>\n",
-       "      <td>0.7568</td>\n",
-       "      <td>0.0405</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.6950</td>\n",
-       "      <td>0.0421</td>\n",
-       "      <td>0.7548</td>\n",
-       "      <td>0.0399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.6948</td>\n",
-       "      <td>0.0317</td>\n",
-       "      <td>0.7476</td>\n",
-       "      <td>0.0384</td>\n",
+       "      <th>Baseline</th>\n",
+       "      <td>0.6446 ± 0.0963</td>\n",
+       "      <td>0.6352 ± 0.1622</td>\n",
+       "      <td>0.5771 ± 0.1745</td>\n",
+       "      <td>0.5851 ± 0.1298</td>\n",
+       "      <td>0.6921 ± 0.1016</td>\n",
+       "      <td>0.2889 ± 0.1881</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.6939</td>\n",
-       "      <td>0.0314</td>\n",
-       "      <td>0.7477</td>\n",
-       "      <td>0.0384</td>\n",
+       "      <th>Site Only</th>\n",
+       "      <td>0.6566 ± 0.0748</td>\n",
+       "      <td>0.6355 ± 0.1207</td>\n",
+       "      <td>0.5999 ± 0.0996</td>\n",
+       "      <td>0.6112 ± 0.0920</td>\n",
+       "      <td>0.6893 ± 0.1115</td>\n",
+       "      <td>0.3090 ± 0.1516</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.6936</td>\n",
-       "      <td>0.0318</td>\n",
-       "      <td>0.7470</td>\n",
-       "      <td>0.0388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0.6934</td>\n",
-       "      <td>0.0325</td>\n",
-       "      <td>0.7482</td>\n",
-       "      <td>0.0385</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>0.6934</td>\n",
-       "      <td>0.0318</td>\n",
-       "      <td>0.7479</td>\n",
-       "      <td>0.0384</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0.6886</td>\n",
-       "      <td>0.0373</td>\n",
-       "      <td>0.7515</td>\n",
-       "      <td>0.0388</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>0.6870</td>\n",
-       "      <td>0.0360</td>\n",
-       "      <td>0.7466</td>\n",
-       "      <td>0.0373</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0.5612</td>\n",
-       "      <td>0.0225</td>\n",
-       "      <td>0.6736</td>\n",
-       "      <td>0.0655</td>\n",
+       "      <th>All Phenotypes</th>\n",
+       "      <td>0.6567 ± 0.0927</td>\n",
+       "      <td>0.6216 ± 0.1328</td>\n",
+       "      <td>0.5834 ± 0.1743</td>\n",
+       "      <td>0.5909 ± 0.1595</td>\n",
+       "      <td>0.6898 ± 0.0978</td>\n",
+       "      <td>0.2909 ± 0.1869</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "      mean_test_accuracy  std_test_accuracy  mean_test_roc_auc  \\\n",
-       "Rank                                                             \n",
-       "0                 0.6966             0.0434             0.7568   \n",
-       "1                 0.6950             0.0421             0.7548   \n",
-       "2                 0.6948             0.0317             0.7476   \n",
-       "3                 0.6939             0.0314             0.7477   \n",
-       "4                 0.6936             0.0318             0.7470   \n",
-       "5                 0.6934             0.0325             0.7482   \n",
-       "6                 0.6934             0.0318             0.7479   \n",
-       "7                 0.6886             0.0373             0.7515   \n",
-       "8                 0.6870             0.0360             0.7466   \n",
-       "9                 0.5612             0.0225             0.6736   \n",
-       "\n",
-       "      std_test_roc_auc  \n",
-       "Rank                    \n",
-       "0               0.0405  \n",
-       "1               0.0399  \n",
-       "2               0.0384  \n",
-       "3               0.0384  \n",
-       "4               0.0388  \n",
-       "5               0.0385  \n",
-       "6               0.0384  \n",
-       "7               0.0388  \n",
-       "8               0.0373  \n",
-       "9               0.0655  "
+       "                       Accuracy        Precision           Recall  \\\n",
+       "Model                                                               \n",
+       "Baseline        0.6446 ± 0.0963  0.6352 ± 0.1622  0.5771 ± 0.1745   \n",
+       "Site Only       0.6566 ± 0.0748  0.6355 ± 0.1207  0.5999 ± 0.0996   \n",
+       "All Phenotypes  0.6567 ± 0.0927  0.6216 ± 0.1328  0.5834 ± 0.1743   \n",
+       "\n",
+       "                             F1            AUROC              MCC  \n",
+       "Model                                                              \n",
+       "Baseline        0.5851 ± 0.1298  0.6921 ± 0.1016  0.2889 ± 0.1881  \n",
+       "Site Only       0.6112 ± 0.0920  0.6893 ± 0.1115  0.3090 ± 0.1516  \n",
+       "All Phenotypes  0.5909 ± 0.1595  0.6898 ± 0.0978  0.2909 ± 0.1869  "
       ]
      },
-     "execution_count": 18,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "cv_results"
+    "# Compile the cross-validation results into a summary table,\n",
+    "# sorting by the model with the highest test accuracy across CV folds\n",
+    "compiled_results = compile_results(cv_results, \"accuracy\")\n",
+    "\n",
+    "# Display the compiled results DataFrame (models as rows, metrics as formatted strings)\n",
+    "display(compiled_results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02820ae0-31ab-42b7-8343-cd330f1331fa",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Interpretation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0523ad8a-cf8f-4984-982a-a7d23dddeeb8",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "We interpret the trained models by analyzing the learned weights associated with functional connectivity features. Specifically, we extract the top-weighted ROI pairs that contributed most to the classification decision.\n",
+    "\n",
+    "These weights are visualized as a **connectome plot**, allowing us to examine which brain region interactions are most informative for distinguishing individuals with autism from controls. This not only enhances the interpretability of the model but also provides potential insights into neurobiological patterns relevant to autism."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "7529a360-33d4-46fe-ba45-f414e7e8d6c4",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[get_dataset_dir] Dataset found in /home/zarizky/nilearn_data/aal_SPM12\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<nilearn.plotting.displays._projectors.OrthoProjector at 0x7f03fca42d10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 660x350 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from nilearn.datasets import fetch_atlas_aal\n",
+    "from nilearn.plotting import find_parcellation_cut_coords\n",
+    "\n",
+    "# Retrieve the trained model configured with all phenotypic metadata\n",
+    "model = trainers[\"all_phenotypes\"]\n",
+    "\n",
+    "# Fetch the AAL (Automated Anatomical Labeling) atlas\n",
+    "# Provides ROI labels and brain region maps\n",
+    "atlas = fetch_atlas_aal()\n",
+    "\n",
+    "# Extract 3D MNI coordinates for each ROI in the AAL atlas\n",
+    "coords = find_parcellation_cut_coords(atlas.maps)\n",
+    "\n",
+    "# Extract weights from the MIDA-transformed feature space (domain-adapted representation)\n",
+    "mida_weights = model.best_mida_.orig_coef_\n",
+    "\n",
+    "# Extract weights from the final classifier (e.g., logistic regression coefficients)\n",
+    "classifier_weights = np.squeeze(model.best_estimator_.coef_, axis=0)\n",
+    "\n",
+    "# Compute final feature-level weights by linearly combining MIDA and classifier weights\n",
+    "# Resulting in one scalar per connectivity feature\n",
+    "weights = (mida_weights @ classifier_weights).T\n",
+    "\n",
+    "# Visualize the top 0.2% strongest ROI-to-ROI connections using a connectome plot\n",
+    "proj = visualize_connectome(\n",
+    "    weights=weights,\n",
+    "    labels=atlas.labels,       # ROI names from the AAL atlas\n",
+    "    coords=coords,             # ROI spatial coordinates (MNI space)\n",
+    "    p=0.002,                   # Visualize top 0.2% of weights by magnitude\n",
+    "    legend_params=dict(\n",
+    "        title=\"Region of Interest\",   # Title shown above the legend\n",
+    "        ncols=3,                      # Organize legend entries into 3 columns\n",
+    "        loc=\"lower center\",          # Legend anchor point\n",
+    "        bbox_to_anchor=(1.5, -1.0),  # Adjust legend position\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "# Display the resulting connectome plot\n",
+    "display(proj)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea959ab0-f8f9-4bd6-866f-658963977abf",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "This plot shows the **most discriminative ROI connections** for classifying ASD vs Control subjects.\n",
+    "- **Red edges** indicate connections **stronger in ASD**.\n",
+    "- **Blue edges** indicate connections **stronger in Control**.\n",
+    "- Color intensity reflects the **magnitude of contribution** to the model’s decision.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Key Patterns**:\n",
+    "- **Increased connectivity in ASD (Red**):\n",
+    "    - Between **frontal regions** (*Frontal_Inf_Tri_L*, *Frontal_Sup_R*) and **limbic structures** (*Hippocampus_R*, *Amygdala_L*), suggesting atypical emotional or executive function coupling.\n",
+    "    - **Temporal and subcortical regions** (*Temporal_Pole_Mid_R*, *Putamen_R*) are also prominent—linked to altered language, reward, or sensorimotor integration in ASD.\n",
+    "- **Increased connectivity in Control (Blue)**:\n",
+    "    - Involving **parietal and sensorimotor regions** (*Postcentral_R*, *Precuneus_R*, *Supramarginal_R*), indicating more typical integration of sensory and motor pathways.\n",
+    "    - Also includes **default mode and association regions** (*Angular_L*, *Parietal_Inf_R*), which are often underconnected in ASD.\n",
+    "\n",
+    "The model distinguishes ASD from Control subjects by identifying **abnormal functional connections**, especially across **frontal–limbic**, **temporal–subcortical**, and **parietal–sensorimotor networks**, aligning with known neurodevelopmental differences in autism."
    ]
   }
  ],