diff --git a/docs/tutorials/runtime_comparison.ipynb b/docs/tutorials/runtime_comparison.ipynb
new file mode 100644
index 0000000..38d1021
--- /dev/null
+++ b/docs/tutorials/runtime_comparison.ipynb
@@ -0,0 +1,404 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import time\n",
+    "\n",
+    "from sklearn.datasets import load_digits, fetch_openml\n",
+    "from sklearn.utils import check_random_state\n",
+    "\n",
+    "from sklearn.ensemble import RandomForestClassifier as RF\n",
+    "from oblique_forests.sporf import ObliqueForestClassifier as SPORF\n",
+    "from oblique_forests.morf import Conv2DObliqueForestClassifier as MORF\n",
+    "\n",
+    "sns.set_palette('Set1')\n",
+    "mpl.rcParams.update({\n",
+    "    \"axes.titlesize\": \"xx-large\",\n",
+    "    \"axes.spines.top\": False,\n",
+    "    \"axes.spines.right\": False,\n",
+    "    \"xtick.bottom\": False,\n",
+    "    \"ytick.left\": False,\n",
+    "    \"image.cmap\": \"inferno\",\n",
+    "})\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Digits Dataset\n",
+    "These are 8x8 images of handwritten digits from `sklearn.datasets`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(100, 64) (100,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "\n",
+    "images, labels = load_digits(return_X_y=True)\n",
+    "\n",
+    "# Get 100 samples of 3s and 5s\n",
+    "n = 100\n",
+    "threes = np.where(labels == 3)[0][:(n // 2)]\n",
+    "fives = np.where(labels == 5)[0][:(n // 2)]\n",
+    "idx = np.concatenate((threes, fives))\n",
+    "\n",
+    "# Subset train data\n",
+    "X = images[idx]\n",
+    "y = labels[idx]\n",
+    "\n",
+    "# Apply random shuffling\n",
+    "permuted_idx = np.random.permutation(len(idx))\n",
+    "X = X[permuted_idx]\n",
+    "y = y[permuted_idx]\n",
+    "\n",
+    "print(X.shape, y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "avg_3 = images[threes].mean(axis=0).reshape(8, 8)\n",
+    "avg_5 = images[fives].mean(axis=0).reshape(8, 8)\n",
+    "diff = np.abs(avg_3 - avg_5)\n",
+    "\n",
+    "axs[0].imshow(avg_3)\n",
+    "axs[0].set_title(\"Average 3\")\n",
+    "\n",
+    "axs[1].imshow(avg_5)\n",
+    "axs[1].set_title(\"Average 5\")\n",
+    "\n",
+    "axs[2].imshow(diff)\n",
+    "axs[2].set_title(\"Absolute Difference\")\n",
+    "for ax in axs:\n",
+    "    ax.tick_params(\n",
+    "        axis=\"both\",\n",
+    "        which=\"both\",\n",
+    "        bottom=False,\n",
+    "        left=False,\n",
+    "        labelbottom=False,\n",
+    "        labelleft=False,\n",
+    "    )\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "source": [
+    "## `RF` vs `SPORF` runtimes"
+   ],
+   "cell_type": "markdown",
+   "metadata": {}
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def time_clf_digits(clf, ns, random_state=None):\n",
+    "    runtimes = np.empty(len(ns))\n",
+    "    rng = check_random_state(random_state)\n",
+    "    images, labels = load_digits(return_X_y=True)\n",
+    "    for i, n in enumerate(ns):\n",
+    "        # Get only 3s and 5s\n",
+    "        threes = np.where(labels == 3)[0][:(n // 2)]\n",
+    "        fives = np.where(labels == 5)[0][:(n // 2)]\n",
+    "        idx = np.concatenate((threes, fives))\n",
+    "        X = images[idx]\n",
+    "        y = labels[idx]\n",
+    "\n",
+    "        # Shuffle samples\n",
+    "        permuted_idx = rng.permutation(len(idx))\n",
+    "        X = X[permuted_idx].reshape(n, -1)\n",
+    "        y = y[permuted_idx].reshape(n)\n",
+    "\n",
+    "        # Begin timing\n",
+    "        start = time.time()\n",
+    "        clf.fit(X, y)\n",
+    "        runtimes[i] = time.time() - start\n",
+    "    return runtimes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rename_clf(clf):\n",
+    "    if isinstance(clf, RF):\n",
+    "        return \"RF\"\n",
+    "    elif isinstance(clf, SPORF):\n",
+    "        return \"SPORF\"\n",
+    "    elif isinstance(clf, MORF):\n",
+    "        return \"MORF\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ns = [10, 20, 40, 50, 100, 150, 200, 300]\n",
+    "\n",
+    "clfs = [\n",
+    "    RF(random_state=0, n_jobs=1),\n",
+    "    SPORF(random_state=0, n_jobs=1),\n",
+    "    # MORF(random_state=0, image_height=8, image_width=8, n_jobs=1)  # Too slow\n",
+    "]\n",
+    "\n",
+    "runtimes = []\n",
+    "for clf in clfs:\n",
+    "    runtimes.append(time_clf_digits(clf, ns, random_state=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "for runtime, clf in zip(runtimes, clfs):\n",
+    "    ax.plot(ns, runtime, label=rename_clf(clf))\n",
+    "ax.legend()\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MNIST Dataset\n",
+    "These are 28x28 images of handwritten digits from `keras.datasets`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(100, 784) (100,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import fetch_openml\n",
+    "\n",
+    "images, labels = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False)\n",
+    "\n",
+    "n = 100\n",
+    "threes = np.where(labels == '3')[0][:(n // 2)]\n",
+    "fives = np.where(labels == '5')[0][:(n // 2)]\n",
+    "idx = np.concatenate((threes, fives))\n",
+    "\n",
+    "# Subset train data\n",
+    "X = images[idx]\n",
+    "y = labels[idx]\n",
+    "\n",
+    "# Apply random shuffling\n",
+    "permuted_idx = np.random.permutation(len(idx))\n",
+    "X = X[permuted_idx]\n",
+    "y = y[permuted_idx]\n",
+    "\n",
+    "print(X.shape, y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 864x288 with 3 Axes>",
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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "avg_3 = images[threes].mean(axis=0).reshape(28, 28)\n",
+    "avg_5 = images[fives].mean(axis=0).reshape(28, 28)\n",
+    "diff = np.abs(avg_3 - avg_5)\n",
+    "\n",
+    "axs[0].imshow(avg_3)\n",
+    "axs[0].set_title(\"Average 3\")\n",
+    "\n",
+    "axs[1].imshow(avg_5)\n",
+    "axs[1].set_title(\"Average 5\")\n",
+    "\n",
+    "axs[2].imshow(diff)\n",
+    "axs[2].set_title(\"Absolute Difference\")\n",
+    "for ax in axs:\n",
+    "    ax.tick_params(\n",
+    "        axis=\"both\",\n",
+    "        which=\"both\",\n",
+    "        bottom=False,\n",
+    "        left=False,\n",
+    "        labelbottom=False,\n",
+    "        labelleft=False,\n",
+    "    )\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def time_clf_mnist(clf, ns, random_state=None):\n",
+    "    runtimes = np.empty(len(ns))\n",
+    "    rng = check_random_state(random_state)\n",
+    "    images, labels = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False)\n",
+    "    for i, n in enumerate(ns):\n",
+    "        # Get only 3s and 5s\n",
+    "        threes = np.where(labels == '3')[0][:(n // 2)]\n",
+    "        fives = np.where(labels == '5')[0][:(n // 2)]\n",
+    "        idx = np.concatenate((threes, fives))\n",
+    "        X = images[idx]\n",
+    "        y = labels[idx]\n",
+    "\n",
+    "        # Shuffle samples\n",
+    "        permuted_idx = rng.permutation(len(idx))\n",
+    "        X = X[permuted_idx]\n",
+    "        y = y[permuted_idx]\n",
+    "\n",
+    "        # Begin timing\n",
+    "        start = time.time()\n",
+    "        clf.fit(X, y)\n",
+    "        runtimes[i] = time.time() - start\n",
+    "    return runtimes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ns = [10, 20, 40, 50, 100, 150, 200, 300, 500]\n",
+    "\n",
+    "clfs = [\n",
+    "    RF(random_state=0, n_jobs=1),\n",
+    "    SPORF(random_state=0, n_jobs=1),\n",
+    "    # MORF(random_state=0, image_height=8, image_width=8, n_jobs=1)  # Too slow\n",
+    "]\n",
+    "\n",
+    "runtimes = []\n",
+    "for clf in clfs:\n",
+    "    runtimes.append(time_clf_mnist(clf, ns, random_state=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "for runtime, clf in zip(runtimes, clfs):\n",
+    "    ax.plot(ns, runtime, label=rename_clf(clf))\n",
+    "ax.legend()\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "python380jvsc74a57bd039ca1c7a169e56d6a333ccd59f8c6786beb2b8f5c3cc68b80d4610822621472b",
+   "display_name": "Python 3.8.0 64-bit ('ProgLearn': conda)"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/docs/tutorials/test_feature_importance.ipynb b/docs/tutorials/test_feature_importance.ipynb
new file mode 100644
index 0000000..db90ff5
--- /dev/null
+++ b/docs/tutorials/test_feature_importance.ipynb
@@ -0,0 +1,529 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Feature Importance\n",
+    "One of the benefits to decision trees is that their results are fairly interpretable in that they allow for estimation of the relative importance of each feature.\n",
+    "\n",
+    "There are many approaches that have been suggested for quantifying importances. The `scikit-learn` implementation of the random forest quantifies these importances using the Gini impurity. For `SPORF` and `MORF`, we use a projection forest specific metric to quantify feature importance by computing the normalized count of the number of times a feature $k$ was used in projections across the ensemble of decision trees.\n",
+    "\n",
+    "Specifically, consider our forest $\\mathcal{T}$ to be a collection of decision trees $\\{T_i\\}_{i=1}^n$, where each decision tree $T_i \\in \\mathcal{T}$ is composed of many nodes $j$. Let $\\mathcal{A}$ be the set of unique atoms across all nodes and all trees in our forest. For each feature $k$, its importance $\\pi_k$ is computed as the number of times an atom assigns it a nonzero weighting, followed by a normalization of $|\\mathcal{A}|$.\n",
+    "$$\n",
+    "\\pi_k = \\frac{1}{|\\mathcal{A}|} \\sum_{T_i \\in \\mathcal{T}} \\sum_{j \\in T_i} \\mathbb{I}(a_{jk} \\not= 0)\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from oblique_forests.tree.oblique_tree import ObliqueTreeClassifier as OTC\n",
+    "from oblique_forests.sporf import ObliqueForestClassifier as SPORF\n",
+    "from oblique_forests.morf import Conv2DObliqueForestClassifier as MORF\n",
+    "\n",
+    "import matplotlib as mpl\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "\n",
+    "mpl.rcParams.update({\n",
+    "    \"axes.titlesize\": \"xx-large\",\n",
+    "    \"axes.spines.bottom\": False,\n",
+    "    \"axes.spines.left\": False,\n",
+    "    \"xtick.bottom\": False,\n",
+    "    \"ytick.left\": False,\n",
+    "    \"image.cmap\": \"inferno\",\n",
+    "    \"image.aspect\": 1\n",
+    "})\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Digits Dataset\n",
+    "We visualize feature importances identified by `RF`, `SPORF`, and `MORF` on a subset of the MNIST dataset. We only consider threes and fives and use 100 8x8 images from each class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(1797, 64) (1797,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.datasets import load_digits\n",
+    "\n",
+    "images, labels = load_digits(return_X_y=True)\n",
+    "print(images.shape, labels.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(200, 64) (200,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get 100 samples of 3s and 5s\n",
+    "num = 100\n",
+    "threes = np.where(labels == 3)[0][:num]\n",
+    "fives = np.where(labels == 5)[0][:num]\n",
+    "idx = np.concatenate((threes, fives))\n",
+    "\n",
+    "# Subset train data\n",
+    "X = images[idx]\n",
+    "y = labels[idx]\n",
+    "\n",
+    "# Apply random shuffling\n",
+    "permuted_idx = np.random.permutation(len(idx))\n",
+    "X = X[permuted_idx]\n",
+    "y = y[permuted_idx]\n",
+    "\n",
+    "print(X.shape, y.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
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\n"
+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "avg_3 = images[threes].mean(axis=0).reshape(8, 8)\n",
+    "avg_5 = images[fives].mean(axis=0).reshape(8, 8)\n",
+    "diff = np.abs(avg_3 - avg_5)\n",
+    "\n",
+    "axs[0].imshow(avg_3)\n",
+    "axs[0].set_title(\"Average 3\")\n",
+    "\n",
+    "axs[1].imshow(avg_5)\n",
+    "axs[1].set_title(\"Average 5\")\n",
+    "\n",
+    "axs[2].imshow(diff)\n",
+    "axs[2].set_title(\"Absolute Difference\")\n",
+    "for ax in axs:\n",
+    "    ax.tick_params(\n",
+    "        axis=\"both\",\n",
+    "        which=\"both\",\n",
+    "        bottom=False,\n",
+    "        left=False,\n",
+    "        labelbottom=False,\n",
+    "        labelleft=False,\n",
+    "    )\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clfs = [\n",
+    "    RandomForestClassifier(random_state=0),\n",
+    "    SPORF(random_state=0),\n",
+    "    MORF(random_state=0, image_height=8, image_width=8)\n",
+    "]\n",
+    "for clf in clfs:\n",
+    "    clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rename_clf(clf):\n",
+    "    if isinstance(clf, RandomForestClassifier):\n",
+    "        return \"RF\"\n",
+    "    elif isinstance(clf, SPORF):\n",
+    "        return \"SPORF\"\n",
+    "    elif isinstance(clf, MORF):\n",
+    "        return \"MORF\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "error",
+     "ename": "AttributeError",
+     "evalue": "'Conv2DObliqueTreeClassifier' object has no attribute 'tree_'",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-bf02f18d0832>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclfs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mimportances\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimportances\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'inferno'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msquare\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     ax.tick_params(\n",
+      "\u001b[0;32m/opt/anaconda3/envs/ProgLearn/lib/python3.8/site-packages/oblique_forests/morf.py\u001b[0m in \u001b[0;36mfeature_importances_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    208\u001b[0m         \u001b[0;31m# 1. Find all unique atoms in the forest\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    209\u001b[0m         \u001b[0;31m# 2. Compute number of times each atom appears across all trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 210\u001b[0;31m         forest_projections = [\n\u001b[0m\u001b[1;32m    211\u001b[0m             \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj_vec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mtree\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/anaconda3/envs/ProgLearn/lib/python3.8/site-packages/oblique_forests/morf.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    211\u001b[0m             \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj_vec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mtree\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestimators_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 213\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnode_count\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    214\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mnode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mproj_vec\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Conv2DObliqueTreeClassifier' object has no attribute 'tree_'"
+     ]
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(19, 4))\n",
+    "\n",
+    "for clf, ax in zip(clfs, axs):\n",
+    "    importances = clf.feature_importances_\n",
+    "    sns.heatmap(importances.reshape(8, 8), cmap='inferno', square=True, ax=ax)\n",
+    "    ax.tick_params(\n",
+    "        axis=\"both\",\n",
+    "        which=\"both\",\n",
+    "        bottom=False,\n",
+    "        left=False,\n",
+    "        labelbottom=False,\n",
+    "        labelleft=False,\n",
+    "    )\n",
+    "    ax.set_title(f\"{rename_clf(clf)} Importance\")\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## MNIST Dataset\n",
+    "We visualize feature importances identified by `RF`, `SPORF`, and `MORF` on a subset of the MNIST dataset. We only consider threes and fives and use 100 28x28 images from each class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(60000, 28, 28)\n(60000,)\n(10000, 28, 28)\n(10000,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# from sklearn.datasets import fetch_openml\n",
+    "from keras.datasets import mnist\n",
+    "\n",
+    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
+    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": [
+      "(200, 28, 28)\n(200,)\n(1010, 28, 28)\n(1010,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Get 100 samples of 3s and 5s\n",
+    "num = 100\n",
+    "threes = np.where(y_train == 3)[0][:num]\n",
+    "fives = np.where(y_train == 5)[0][:num]\n",
+    "train_idx = np.concatenate((threes, fives))\n",
+    "\n",
+    "# Subset train data\n",
+    "Xtrain = X_train[train_idx]\n",
+    "ytrain = y_train[train_idx]\n",
+    "\n",
+    "# Apply random shuffling\n",
+    "permuted_idx = np.random.permutation(len(train_idx))\n",
+    "Xtrain = Xtrain[permuted_idx]\n",
+    "ytrain = ytrain[permuted_idx]\n",
+    "\n",
+    "# Subset test data\n",
+    "test_idx = np.where(y_test == 3)[0]\n",
+    "Xtest = X_test[test_idx]\n",
+    "ytest = y_test[test_idx]\n",
+    "\n",
+    "print(Xtrain.shape, ytrain.shape, Xtest.shape, ytest.shape, sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 864x288 with 3 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg height=\"290.179512pt\" version=\"1.1\" viewBox=\"0 0 839.849412 290.179512\" width=\"839.849412pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-04-21T03:26:15.950727</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.4.1, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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+     },
+     "metadata": {}
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(12, 4))\n",
+    "avg_3 = X_train[threes].mean(axis=0)\n",
+    "avg_5 = X_train[fives].mean(axis=0)\n",
+    "diff = np.abs(avg_3 - avg_5)\n",
+    "\n",
+    "axs[0].imshow(avg_3)\n",
+    "axs[0].set_title(\"Average 3\")\n",
+    "\n",
+    "axs[1].imshow(avg_5)\n",
+    "axs[1].set_title(\"Average 5\")\n",
+    "\n",
+    "axs[2].imshow(diff)\n",
+    "axs[2].set_title(\"Absolute Difference\")\n",
+    "for ax in axs:\n",
+    "    ax.tick_params(\n",
+    "        axis=\"both\",\n",
+    "        which=\"both\",\n",
+    "        bottom=False,\n",
+    "        left=False,\n",
+    "        labelbottom=False,\n",
+    "        labelleft=False,\n",
+    "    )\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "RandomForestClassifier(random_state=0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 20
+    }
+   ],
+   "source": [
+    "clf = RandomForestClassifier(random_state=0)\n",
+    "clf.fit(Xtrain.reshape(Xtrain.shape[0], -1), ytrain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rename_clf(clf):\n",
+    "    if isinstance(clf, RandomForestClassifier):\n",
+    "        return \"RF\"\n",
+    "    elif isinstance(clf, SPORF):\n",
+    "        return \"SPORF\"\n",
+    "    elif isinstance(clf, MORF):\n",
+    "        return \"MORF\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 360x288 with 2 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg height=\"279.809006pt\" version=\"1.1\" viewBox=\"0 0 319.129525 279.809006\" width=\"319.129525pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-04-21T03:26:17.992359</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.4.1, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "importances = clf.feature_importances_\n",
+    "sns.heatmap(importances.reshape(28, 28), cmap='inferno', square=True)\n",
+    "ax.tick_params(\n",
+    "    axis=\"both\",\n",
+    "    which=\"both\",\n",
+    "    bottom=False,\n",
+    "    left=False,\n",
+    "    labelbottom=False,\n",
+    "    labelleft=False,\n",
+    ")\n",
+    "ax.set_title(f\"{rename_clf(clf)} Importance\")\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "output_type": "execute_result",
+     "data": {
+      "text/plain": [
+       "ObliqueForestClassifier(random_state=0)"
+      ]
+     },
+     "metadata": {},
+     "execution_count": 25
+    }
+   ],
+   "source": [
+    "clf = SPORF(n_estimators=100, random_state=0)\n",
+    "clf.fit(Xtrain.reshape(Xtrain.shape[0], -1), ytrain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "display_data",
+     "data": {
+      "text/plain": "<Figure size 360x288 with 2 Axes>",
+      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg height=\"279.809006pt\" version=\"1.1\" viewBox=\"0 0 319.129525 279.809006\" width=\"319.129525pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-04-21T03:28:42.502439</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.4.1, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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\n"
+     },
+     "metadata": {
+      "needs_background": "light"
+     }
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(5, 4))\n",
+    "importances = clf.feature_importances_\n",
+    "sns.heatmap(importances.reshape(28, 28), cmap='inferno', square=True)\n",
+    "ax.tick_params(\n",
+    "    axis=\"both\",\n",
+    "    which=\"both\",\n",
+    "    bottom=False,\n",
+    "    left=False,\n",
+    "    labelbottom=False,\n",
+    "    labelleft=False,\n",
+    ")\n",
+    "ax.set_title(f\"{rename_clf(clf)} Importance\")\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# morf = MORF(random_state=0, image_height=28, image_width=28)\n",
+    "# morf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# XXX: MORF fits too slowly\n",
+    "clfs = [\n",
+    "    RandomForestClassifier(random_state=0),\n",
+    "    SPORF(random_state=0),\n",
+    "    MORF(random_state=0, image_height=28, image_width=28)\n",
+    "]\n",
+    "for clf in clfs:\n",
+    "    clf.fit(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axs = plt.subplots(1, 3, figsize=(19, 4))\n",
+    "\n",
+    "for clf, ax in zip(clfs, ax):\n",
+    "    importances = clf.feature_importances_\n",
+    "    sns.heatmap(importances.reshape(28, 28), cmap='inferno', square=True, ax=ax)\n",
+    "    ax.tick_params(\n",
+    "        axis=\"both\",\n",
+    "        which=\"both\",\n",
+    "        bottom=False,\n",
+    "        left=False,\n",
+    "        labelbottom=False,\n",
+    "        labelleft=False,\n",
+    "    )\n",
+    "    ax.set_title(f\"{rename_clf(clf)} Importance\")\n",
+    "fig.tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "name": "python380jvsc74a57bd039ca1c7a169e56d6a333ccd59f8c6786beb2b8f5c3cc68b80d4610822621472b",
+   "display_name": "Python 3.8.0 64-bit ('ProgLearn': conda)"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
\ No newline at end of file
diff --git a/oblique_forests/morf.py b/oblique_forests/morf.py
index a32a121..b2f8fdd 100644
--- a/oblique_forests/morf.py
+++ b/oblique_forests/morf.py
@@ -1,4 +1,8 @@
+import numpy as np
+from joblib import Parallel, delayed
 from sklearn.ensemble._forest import ForestClassifier
+from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.fixes import _joblib_parallel_args
 
 from .tree.morf_tree import Conv2DObliqueTreeClassifier
 
@@ -175,11 +179,55 @@ def __init__(
         # self.min_impurity_split = min_impurity_split
 
         # s-rerf params
-        # self.discontiguous_height = discontiguous_height
-        # self.discontiguous_width = discontiguous_width
-        # self.image_height = image_height
-        # self.image_width = image_width
-        # self.patch_height_max = patch_height_max
-        # self.patch_height_min = patch_height_min
-        # self.patch_width_max = patch_width_max
-        # self.patch_width_min = patch_width_min
+        self.discontiguous_height = discontiguous_height
+        self.discontiguous_width = discontiguous_width
+        self.image_height = image_height
+        self.image_width = image_width
+        self.patch_height_max = patch_height_max
+        self.patch_height_min = patch_height_min
+        self.patch_width_max = patch_width_max
+        self.patch_width_min = patch_width_min
+
+    @property
+    def feature_importances_(self):
+        """
+        Computes the importance of every unique feature used to make a split
+        in each tree of the forest.
+
+        Parameters
+        ----------
+        normalize : bool, default=True
+            A boolean to indicate whether to normalize feature importances.
+
+        Returns
+        -------
+        importances : array of shape [n_features]
+            Array of count-based feature importances.
+        """
+        # TODO: Parallelize this and see if there is an equivalent way to express this better
+        # 1. Find all unique atoms in the forest
+        # 2. Compute number of times each atom appears across all trees
+        forest_projections = [
+            node.proj_vec
+            for tree in self.estimators_
+            if tree.tree_.node_count > 0
+            for node in tree.tree_.nodes
+            if node.proj_vec is not None
+        ]
+        unique_projections, counts = np.unique(
+            forest_projections, axis=0, return_counts=True
+        )
+
+        if counts.sum() == 0:
+            return np.zeros(self.n_features_, dtype=np.float64)
+
+        # 3. Count how many times each feature gets nonzero weight in unique projections
+        importances = np.zeros(self.n_features_)
+        for proj_vec, count in zip(unique_projections, counts):
+            importances[np.nonzero(proj_vec)] += count
+        
+        # 4. Normalize by number of unique projections
+        if len(unique_projections) > 0:
+            importances /= len(unique_projections)
+
+        return importances
diff --git a/oblique_forests/sporf.py b/oblique_forests/sporf.py
index 557ae6d..515ad4e 100644
--- a/oblique_forests/sporf.py
+++ b/oblique_forests/sporf.py
@@ -1,6 +1,10 @@
+import numpy as np
+from joblib import Parallel, delayed
 from sklearn.ensemble._forest import ForestClassifier
+from sklearn.utils.validation import check_is_fitted
+from sklearn.utils.fixes import _joblib_parallel_args
 
-from .tree.oblique_tree import ObliqueTreeClassifier
+from oblique_forests.tree.oblique_tree import ObliqueTreeClassifier
 
 
 class ObliqueForestClassifier(ForestClassifier):
@@ -95,3 +99,47 @@ def __init__(
         # self.max_leaf_nodes = max_leaf_nodes
         # self.min_impurity_decrease = min_impurity_decrease
         # self.min_impurity_split = min_impurity_split
+
+    @property
+    def feature_importances_(self):
+        """
+        Computes the importance of every unique feature used to make a split
+        in each tree of the forest.
+
+        Parameters
+        ----------
+        normalize : bool, default=True
+            A boolean to indicate whether to normalize feature importances.
+
+        Returns
+        -------
+        importances : array of shape [n_features]
+            Array of count-based feature importances.
+        """
+        # TODO: Parallelize this and see if there is an equivalent way to express this better
+        # 1. Find all unique atoms in the forest
+        # 2. Compute number of times each atom appears across all trees
+        forest_projections = [
+            node.proj_vec
+            for tree in self.estimators_
+            if tree.tree_.node_count > 0
+            for node in tree.tree_.nodes
+            if node.proj_vec is not None
+        ]
+        unique_projections, counts = np.unique(
+            forest_projections, axis=0, return_counts=True
+        )
+
+        if counts.sum() == 0:
+            return np.zeros((self.n_features_), dtype=np.float64)
+
+        # 3. Count how many times each feature gets nonzero weight in unique projections
+        importances = np.zeros((self.n_features_), dtype=np.float64)
+        for proj_vec, count in zip(unique_projections, counts):
+            importances[np.nonzero(proj_vec)] += count
+
+        # 4. Normalize by number of unique projections
+        if len(unique_projections) > 0:
+            importances /= len(unique_projections)
+
+        return importances
\ No newline at end of file
diff --git a/oblique_forests/tree/morf_tree.py b/oblique_forests/tree/morf_tree.py
index 936cb9a..4dc31aa 100644
--- a/oblique_forests/tree/morf_tree.py
+++ b/oblique_forests/tree/morf_tree.py
@@ -265,7 +265,7 @@ def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None):
         splitter = self._set_splitter(X, y)
 
         # create the Oblique tree
-        self.tree = ObliqueTree(
+        self.tree_ = ObliqueTree(
             splitter,
             self.min_samples_split,
             self.min_samples_leaf,
@@ -273,7 +273,7 @@ def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None):
             self.min_impurity_split,
             self.min_impurity_decrease,
         )
-        self.tree.build()
+        self.tree_.build()
         return self
 
 
diff --git a/oblique_forests/tree/oblique_tree.py b/oblique_forests/tree/oblique_tree.py
index 16bf622..d439cab 100644
--- a/oblique_forests/tree/oblique_tree.py
+++ b/oblique_forests/tree/oblique_tree.py
@@ -3,6 +3,7 @@
 from joblib import Parallel, delayed
 from sklearn.base import BaseEstimator
 from sklearn.utils.validation import check_array, check_is_fitted, check_X_y
+from sklearn.utils.fixes import _joblib_parallel_args
 
 from ._split import BaseObliqueSplitter
 from .oblique_base import BaseManifoldSplitter, Node, SplitInfo, StackRecord
@@ -528,6 +529,37 @@ def predict(self, X, check_input=True):
 
         return predictions
 
+    def compute_feature_importances(self):
+        """
+        Computes the importance of each feature (aka variable).
+
+        Parameters
+        ----------
+        unique_projections : ndarray of shape (n_proj, n_features)
+            Array of unique sampling projection vectors.
+
+        Returns
+        -------
+        importances : ndarray of shape (n_features,)
+            Normalized importance of each feature of the data matrix.
+        """
+        projections = [
+            node.proj_vec for node in self.nodes if node.proj_vec is not None
+        ]
+        unique_projections, counts = np.unique(projections, axis=0, return_counts=True)
+
+        if counts.sum() == 0:
+            return np.zeros((self.splitter.n_features,))
+
+        importances = np.zeros((self.splitter.n_features,))
+        for proj_vec, count in zip(unique_projections, counts):
+            importances[np.nonzero(proj_vec)] += count
+
+        if len(unique_projections) > 0:
+            importances /= len(unique_projections)
+
+        return importances
+
 
 class ObliqueTreeClassifier(BaseEstimator):
     """
@@ -600,6 +632,7 @@ def __init__(
 
         # Max features
         self.max_features = max_features
+        self.n_jobs = n_jobs
 
         self.n_classes = None
         self.n_jobs = n_jobs
@@ -640,7 +673,7 @@ def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None):
         tree_func = self._tree_class()
 
         # instantiate the tree and build it
-        self.tree = tree_func(
+        self.tree_ = tree_func(
             splitter,
             self.min_samples_split,
             self.min_samples_leaf,
@@ -648,7 +681,7 @@ def fit(self, X, y, sample_weight=None, check_input=True, X_idx_sorted=None):
             self.min_impurity_split,
             self.min_impurity_decrease,
         )
-        self.tree.build()
+        self.tree_.build()
 
         return self
 
@@ -666,7 +699,7 @@ def apply(self, X):
         pred_nodes : array of shape[n_samples]
             The indices for each test sample's final node in the oblique tree.
         """
-        pred_nodes = self.tree.predict(X).astype(int)
+        pred_nodes = self.tree_.predict(X).astype(int)
         return pred_nodes
 
     def predict(self, X, check_input=True):
@@ -689,7 +722,7 @@ def predict(self, X, check_input=True):
         pred_nodes = self.apply(X)
         for k in range(len(pred_nodes)):
             id = pred_nodes[k]
-            preds[k] = self.tree.nodes[id].label
+            preds[k] = self.tree_.nodes[id].label
 
         return preds
 
@@ -713,7 +746,7 @@ def predict_proba(self, X, check_input=True):
         pred_nodes = self.apply(X)
         for k in range(len(preds)):
             id = pred_nodes[k]
-            preds[k] = self.tree.nodes[id].proba
+            preds[k] = self.tree_.nodes[id].proba
 
         return preds
 
@@ -737,3 +770,53 @@ def predict_log_proba(self, X, check_input=True):
     # TODO: Actually do this function
     def _validate_X_predict(self, X, check_input=True):
         return X
+
+    @property
+    def feature_importances_(self):
+        """
+        Return the feature importances.
+        The importance of a feature is computed as the number of times it
+        is used in a projection across all split nodes
+
+        Returns
+        -------
+        feature_importances_ : ndarray of shape (n_features,)
+            Array of count-based feature importances.
+        """
+        check_is_fitted(self)
+
+        return self.tree_.compute_feature_importances()
+
+    def compute_projection_counts(self, unique_projections=None):
+        """
+        Counts the number of times each unique projection in the tree appears.
+
+        Parameters
+        ----------
+        unique_projections : ndarray of shape (n_proj,), optional
+            Array of unique projections to count, by default None
+
+        Returns
+        -------
+        projection_counts : ndarray of shape (n_proj,)
+            Counts of each unique projection used in this tree.
+        """
+        check_is_fitted(self)
+
+        if unique_projections is None:
+            projections = [
+                node.proj_vec
+                for node in self.tree_.nodes
+                if node.proj_vec is not None
+            ]
+            unique_projections, counts = np.unique(projections, axis=0, return_counts=True)
+            return counts, unique_projections
+        
+        # TODO: see if joblib will speed up at all for this for loop
+        n_proj = len(unique_projections)
+        counts = np.zeros(n_proj)
+        for node in self.tree_.nodes:
+            projection_idx = np.where((unique_projections == node.proj_vec).all(axis=1))
+            counts[projection_idx] += 1
+        
+        return counts, unique_projections
\ No newline at end of file
diff --git a/oblique_forests/tree/tests/test_morf_tree.py b/oblique_forests/tree/tests/test_morf_tree.py
index 9cbd12c..32c1bf0 100644
--- a/oblique_forests/tree/tests/test_morf_tree.py
+++ b/oblique_forests/tree/tests/test_morf_tree.py
@@ -10,6 +10,10 @@
 from sklearn.utils.validation import check_random_state
 
 from oblique_forests.tree.morf_split import Conv2DSplitter
+from oblique_forests.sporf import ObliqueForestClassifier as SPORF
+from oblique_forests.tree.oblique_tree import ObliqueTreeClassifier as OTC
+from oblique_forests.morf import Conv2DObliqueForestClassifier as MORF
+from oblique_forests.tree.morf_tree import Conv2DObliqueTreeClassifier
 
 # toy sample
 X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
@@ -43,7 +47,7 @@ def test_convolutional_splitter():
     y[:25] = 0
 
     splitter = Conv2DSplitter(
-        X,
+        X.reshape(n, -1),
         y,
         max_features=1,
         feature_combinations=1.5,
@@ -52,4 +56,60 @@ def test_convolutional_splitter():
         image_width=d,
         patch_height_max=2,
         patch_height_min=2,
+        patch_width_max=3,
+        patch_width_min=3,
     )
+
+    splitter.sample_proj_mat(splitter.indices)
+
+
+if __name__ == "__main__":
+
+    test_convolutional_splitter()
+
+    # from sklearn.datasets import fetch_openml
+    from keras.datasets import mnist
+    import time
+
+    (X_train, y_train), (X_test, y_test) = mnist.load_data()
+
+    # Get 100 samples of 3s and 5s
+    num = 100
+    threes = np.where(y_train == 3)[0][:num]
+    fives = np.where(y_train == 5)[0][:num]
+    train_idx = np.concatenate((threes, fives))
+
+    # Subset train data
+    Xtrain = X_train[train_idx]
+    ytrain = y_train[train_idx]
+
+    # Apply random shuffling
+    permuted_idx = np.random.permutation(len(train_idx))
+    Xtrain = Xtrain[permuted_idx]
+    ytrain = ytrain[permuted_idx]
+
+    # Subset test data
+    test_idx = np.where(y_test == 3)[0]
+    Xtest = X_test[test_idx]
+    ytest = y_test[test_idx]
+
+    print(f"-----{2 * num} samples")
+
+    clf = OTC(random_state=0)
+    start = time.time()
+    clf.fit(Xtrain.reshape(Xtrain.shape[0], -1), ytrain)
+    elapsed = time.time() - start
+    print(elapsed)
+    print(f"SPORF Tree: {elapsed} sec")
+
+    clf = Conv2DObliqueTreeClassifier(image_height=28, image_width=28, random_state=0)
+    start = time.time()
+    clf.fit(Xtrain.reshape(Xtrain.shape[0], -1), ytrain)
+    elapsed = time.time() - start
+    print(f"MORF Tree: {elapsed} sec")
+    
+    clf = SPORF(n_estimators=100, random_state=0)
+    start = time.time()
+    clf.fit(Xtrain.reshape(Xtrain.shape[0], -1), ytrain)
+    elapsed = time.time() - start
+    print(f"SPORF: {elapsed} sec")
\ No newline at end of file
diff --git a/oblique_forests/tree/tests/test_splitter.py b/oblique_forests/tree/tests/test_splitter.py
index 4ef97e9..30b2767 100644
--- a/oblique_forests/tree/tests/test_splitter.py
+++ b/oblique_forests/tree/tests/test_splitter.py
@@ -34,16 +34,16 @@ def test_argmin(self):
         assert 4 == j
 
     def test_matmul(self):
-        
+
         b = BOS()
 
         A = np.zeros((3, 3), dtype=np.float64)
         B = np.ones((3, 3), dtype=np.float64)
-        
+
         for i in range(3):
             for j in range(3):
-                A[i, j] = 3*i + j + 1
-        
+                A[i, j] = 3 * i + j + 1
+
         res = b.test_matmul(A, B)
 
         C = np.ones((3, 3), dtype=np.float64)
@@ -53,7 +53,6 @@ def test_matmul(self):
 
         assert_allclose(C, res)
 
-
     def test_impurity(self):
 
         """
diff --git a/oblique_forests/tree/tests/test_sporf.py b/oblique_forests/tree/tests/test_sporf.py
new file mode 100644
index 0000000..921fecd
--- /dev/null
+++ b/oblique_forests/tree/tests/test_sporf.py
@@ -0,0 +1,561 @@
+import numpy as np
+from numpy.testing import (
+    assert_almost_equal,
+    assert_allclose,
+    assert_array_equal,
+    assert_array_almost_equal,
+)
+
+import pytest
+from oblique_forests.tree.oblique_tree import ObliqueTreeClassifier as OTC
+from oblique_forests.sporf import ObliqueForestClassifier as OFC
+
+from sklearn import datasets
+from sklearn.metrics import accuracy_score
+
+"""
+Sklearn test_tree.py stuff
+"""
+X_small = np.array(
+    [
+        [
+            0,
+            0,
+            4,
+            0,
+            0,
+            0,
+            1,
+            -14,
+            0,
+            -4,
+            0,
+            0,
+            0,
+            0,
+        ],
+        [
+            0,
+            0,
+            5,
+            3,
+            0,
+            -4,
+            0,
+            0,
+            1,
+            -5,
+            0.2,
+            0,
+            4,
+            1,
+        ],
+        [
+            -1,
+            -1,
+            0,
+            0,
+            -4.5,
+            0,
+            0,
+            2.1,
+            1,
+            0,
+            0,
+            -4.5,
+            0,
+            1,
+        ],
+        [
+            -1,
+            -1,
+            0,
+            -1.2,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0,
+            0.2,
+            0,
+            0,
+            1,
+        ],
+        [
+            -1,
+            -1,
+            0,
+            0,
+            0,
+            0,
+            0,
+            3,
+            0,
+            0,
+            0,
+            0,
+            0,
+            1,
+        ],
+        [
+            -1,
+            -2,
+            0,
+            4,
+            -3,
+            10,
+            4,
+            0,
+            -3.2,
+            0,
+            4,
+            3,
+            -4,
+            1,
+        ],
+        [
+            2.11,
+            0,
+            -6,
+            -0.5,
+            0,
+            11,
+            0,
+            0,
+            -3.2,
+            6,
+            0.5,
+            0,
+            -3,
+            1,
+        ],
+        [
+            2.11,
+            0,
+            -6,
+            -0.5,
+            0,
+            11,
+            0,
+            0,
+            -3.2,
+            6,
+            0,
+            0,
+            -2,
+            1,
+        ],
+        [
+            2.11,
+            8,
+            -6,
+            -0.5,
+            0,
+            11,
+            0,
+            0,
+            -3.2,
+            6,
+            0,
+            0,
+            -2,
+            1,
+        ],
+        [
+            2.11,
+            8,
+            -6,
+            -0.5,
+            0,
+            11,
+            0,
+            0,
+            -3.2,
+            6,
+            0.5,
+            0,
+            -1,
+            0,
+        ],
+        [
+            2,
+            8,
+            5,
+            1,
+            0.5,
+            -4,
+            10,
+            0,
+            1,
+            -5,
+            3,
+            0,
+            2,
+            0,
+        ],
+        [
+            2,
+            0,
+            1,
+            1,
+            1,
+            -1,
+            1,
+            0,
+            0,
+            -2,
+            3,
+            0,
+            1,
+            0,
+        ],
+        [
+            2,
+            0,
+            1,
+            2,
+            3,
+            -1,
+            10,
+            2,
+            0,
+            -1,
+            1,
+            2,
+            2,
+            0,
+        ],
+        [
+            1,
+            1,
+            0,
+            2,
+            2,
+            -1,
+            1,
+            2,
+            0,
+            -5,
+            1,
+            2,
+            3,
+            0,
+        ],
+        [
+            3,
+            1,
+            0,
+            3,
+            0,
+            -4,
+            10,
+            0,
+            1,
+            -5,
+            3,
+            0,
+            3,
+            1,
+        ],
+        [
+            2.11,
+            8,
+            -6,
+            -0.5,
+            0,
+            1,
+            0,
+            0,
+            -3.2,
+            6,
+            0.5,
+            0,
+            -3,
+            1,
+        ],
+        [
+            2.11,
+            8,
+            -6,
+            -0.5,
+            0,
+            1,
+            0,
+            0,
+            -3.2,
+            6,
+            1.5,
+            1,
+            -1,
+            -1,
+        ],
+        [
+            2.11,
+            8,
+            -6,
+            -0.5,
+            0,
+            10,
+            0,
+            0,
+            -3.2,
+            6,
+            0.5,
+            0,
+            -1,
+            -1,
+        ],
+        [
+            2,
+            0,
+            5,
+            1,
+            0.5,
+            -2,
+            10,
+            0,
+            1,
+            -5,
+            3,
+            1,
+            0,
+            -1,
+        ],
+        [
+            2,
+            0,
+            1,
+            1,
+            1,
+            -2,
+            1,
+            0,
+            0,
+            -2,
+            0,
+            0,
+            0,
+            1,
+        ],
+        [
+            2,
+            1,
+            1,
+            1,
+            2,
+            -1,
+            10,
+            2,
+            0,
+            -1,
+            0,
+            2,
+            1,
+            1,
+        ],
+        [
+            1,
+            1,
+            0,
+            0,
+            1,
+            -3,
+            1,
+            2,
+            0,
+            -5,
+            1,
+            2,
+            1,
+            1,
+        ],
+        [
+            3,
+            1,
+            0,
+            1,
+            0,
+            -4,
+            1,
+            0,
+            1,
+            -2,
+            0,
+            0,
+            1,
+            0,
+        ],
+    ]
+)
+
+y_small = [1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0]
+y_small_reg = [
+    1.0,
+    2.1,
+    1.2,
+    0.05,
+    10,
+    2.4,
+    3.1,
+    1.01,
+    0.01,
+    2.98,
+    3.1,
+    1.1,
+    0.0,
+    1.2,
+    2,
+    11,
+    0,
+    0,
+    4.5,
+    0.201,
+    1.06,
+    0.9,
+    0,
+]
+
+# toy sample
+X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+y = [-1, -1, -1, 1, 1, 1]
+T = [[-1, -1], [2, 2], [3, 2]]
+true_result = [-1, 1, 1]
+
+# also load the iris dataset
+# and randomly permute it
+iris = datasets.load_iris()
+rng = np.random.RandomState(1)
+perm = rng.permutation(iris.target.size)
+iris.data = iris.data[perm]
+iris.target = iris.target[perm]
+
+# also load the diabetes dataset
+# and randomly permute it
+diabetes = datasets.load_diabetes()
+perm = rng.permutation(diabetes.target.size)
+diabetes.data = diabetes.data[perm]
+diabetes.target = diabetes.target[perm]
+
+# Ignoring digits dataset cause it takes a minute
+
+
+def test_classification_toy():
+    # Check classification on a toy dataset.
+    clf = OTC(random_state=0)
+    clf.fit(X, y)
+    assert_array_equal(clf.predict(T), true_result)
+
+    """
+    # Ignoring because max_features implemented differently
+    clf = OTC(max_features=1, random_state=0)
+    clf.fit(X, y)
+    assert_array_equal(clf.predict(T), true_result)
+    """
+
+
+def test_xor():
+
+    # Check on a XOR problem
+    y = np.zeros((10, 10))
+    y[:5, :5] = 1
+    y[5:, 5:] = 1
+
+    gridx, gridy = np.indices(y.shape)
+
+    X = np.vstack([gridx.ravel(), gridy.ravel()]).T
+    y = y.ravel()
+
+    # Changing feature parameters from default 1.5 to 2 makes this test pass.
+    clf = OTC(random_state=0, feature_combinations=2)
+    clf.fit(X, y)
+
+    assert accuracy_score(clf.predict(X), y) == 1
+
+
+def test_iris():
+
+    clf = OTC(random_state=0)
+
+    clf.fit(iris.data, iris.target)
+    score = accuracy_score(clf.predict(iris.data), iris.target)
+    assert score > 0.9
+
+
+def test_diabetes():
+
+    """
+    Diabetes should overfit with MSE = 0 for normal trees.
+    idk if this applies to sporf, so this is just a placeholder
+    to check consistency like iris.
+    """
+
+    clf = OTC(random_state=0)
+
+    clf.fit(diabetes.data, diabetes.target)
+    score = accuracy_score(clf.predict(diabetes.data), diabetes.target)
+    assert score > 0.9
+
+
+def test_probability():
+
+    clf = OTC(random_state=0)
+
+    clf.fit(iris.data, iris.target)
+    p = clf.predict_proba(iris.data)
+
+    assert_array_almost_equal(np.sum(p, 1), np.ones(iris.data.shape[0]))
+
+    assert_array_equal(np.argmax(p, 1), clf.predict(iris.data))
+
+    assert_almost_equal(
+        clf.predict_proba(iris.data), np.exp(clf.predict_log_proba(iris.data))
+    )
+
+
+def test_pure_set():
+
+    clf = OTC(random_state=0)
+
+    X = [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
+    y = [1, 1, 1, 1, 1, 1]
+
+    clf.fit(X, y)
+    assert_array_equal(clf.predict(X), y)
+
+
+def test_importances():
+    # Check variable importances.
+    X, y = datasets.make_classification(
+        n_samples=5000,
+        n_features=10,
+        n_informative=3,
+        n_redundant=0,
+        n_repeated=0,
+        shuffle=False,
+        random_state=0,
+    )
+
+    clf = OTC(random_state=0)
+
+    clf.fit(X, y)
+    importances = clf.feature_importances_
+    n_important = np.sum(importances > 0.4)
+
+    assert importances.shape[0] == 10, "Failed with SPORF"
+    assert n_important == 3, "Failed with SPORF"
+
+    # Check on iris that importances are the same for all builders
+    clf = OTC(random_state=0)
+    clf.fit(iris.data, iris.target)
+    clf2 = OTC(random_state=0, max_depth=len(iris.data))
+    clf2.fit(iris.data, iris.target)
+
+    assert_array_equal(clf.feature_importances_, clf2.feature_importances_)
+
+
+def test_importances_raises():
+    # Check if variable importance before fit raises ValueError.
+    clf = OTC(random_state=0)
+    with pytest.raises(ValueError):
+        getattr(clf, "feature_importances_")
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