chp16_TrainingForDifferentMetrics.ipynb

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   "cell_type": "markdown",
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   "source": [
    "## 16.1 Metric overview\n",
    "\n",
    "The metrics we are going to consider in this chapter that are generally used to evaluate machine learning models include accuracy, precision, recall, F1 score, and the area under the ROC curve.\n",
    "- Accuracy measures how often the model correctly predicts the target class.\n",
    "- Precision measures how often the model is correct when it predicts the positive class.\n",
    "- Recall measures how often the model can correctly identify the positive class.\n",
    "- The area under the ROC curve is one measure to balance precision and recall.\n",
    "- The F1 score combines both precision and recall by using their harmonic mean.\n",
    "\n",
    "These metrics help to evaluate a model's performance and choose the right metric for a specific problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5060b349-0c9c-48fb-81cf-856d2739c9cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dtreeviz\n",
    "from feature_engine import encoding, imputation\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import base, compose, datasets, ensemble, metrics, model_selection, pipeline, preprocessing, tree\n",
    "import scikitplot\n",
    "import xgboost as xgb\n",
    "import yellowbrick.model_selection as ms\n",
    "from yellowbrick import classifier\n",
    "import urllib\n",
    "import zipfile\n",
    "from hyperopt import fmin, tpe, hp, Trials\n",
    "from EffectiveXGBoost import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e198228-8807-4af5-9965-743cca9d36b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "G:\\My Drive\\SisengAI\\AlgorithmicTrading\\code_rebuilding\\MattHarrison\\EffectiveXGBoost\\EffectiveXGBoost_MyTake\\EffectiveXGBoost.py:62: DtypeWarning: Columns (0,2,8,10,21,23,24,25,26,27,28,44,56,64,83,85,87,107,109,123,125,150,157,172,174,194,210,218,219,223,246,249,262,264,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,304,306,325,326,329,341,368,371,384,385,389,390,391,393,394) have mixed types. Specify dtype option on import or set low_memory=False.\n"
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       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "              importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "              importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints=&#x27;()&#x27;, n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)</pre></div></div></div></div></div>"
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      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
       "              early_stopping_rounds=None, enable_categorical=False,\n",
       "              eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
       "              max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
       "              missing=nan, monotone_constraints='()', n_estimators=100,\n",
       "              n_jobs=0, num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, ...)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_url = 'https://github.com/mattharrison/datasets/raw/master/data/kaggle-survey-2018.zip'\n",
    "file_name = 'kaggle-survey-2018.zip'\n",
    "dataset = 'multipleChoiceResponses.csv'\n",
    "\n",
    "raw = extract_dataset(path_url, file_name, dataset)\n",
    "\n",
    "# Create raw X and raw y\n",
    "kag_X, kag_y = prepX_y(raw, 'Q6')\n",
    "\n",
    "# Split data\n",
    "kag_X_train, kag_X_test, kag_y_train, kag_y_test = (model_selection\n",
    "                                                    .train_test_split(kag_X, \n",
    "                                                                      kag_y, \n",
    "                                                                      test_size=.3, \n",
    "                                                                      random_state=42, \n",
    "                                                                      stratify=kag_y)\n",
    "                                                   )\n",
    "\n",
    "\n",
    "# Transform X with pipeline\n",
    "pline = pipeline.Pipeline(\n",
    "    [('tweak', PrepDataTransformer()),\n",
    "    ('cat', encoding.OneHotEncoder(top_categories=5, drop_last=True,\n",
    "    variables=['Q1', 'Q3', 'major'])),\n",
    "    ('num_impute', imputation.MeanMedianImputer(imputation_method='median',\n",
    "                variables=['education', 'years_exp']))]\n",
    "    )\n",
    "\n",
    "X_train = pline.fit_transform(kag_X_train)\n",
    "X_test = pline.transform(kag_X_test)\n",
    "\n",
    "# Transform y with label encoder\n",
    "label_encoder = preprocessing.LabelEncoder()\n",
    "label_encoder.fit(kag_y_train)\n",
    "y_train = label_encoder.transform(kag_y_train)\n",
    "y_test = label_encoder.transform(kag_y_test)\n",
    "\n",
    "# Combined Data for cross validation/etc\n",
    "X = pd.concat([X_train, X_test], axis='index')\n",
    "y = pd.Series([*y_train, *y_test], index=X.index)\n",
    "\n",
    "# Default training\n",
    "xg = xgb.XGBClassifier()\n",
    "xg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "256222ad-3ea3-4757-bf24-78f0e820debb",
   "metadata": {},
   "source": [
    "## 16.2 Training with Validation Curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b516a404-3650-4be2-8d3d-46a962c51992",
   "metadata": {},
   "outputs": [],
   "source": [
    "from yellowbrick import model_selection as ms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "844633cd-3e7e-4327-bb98-632f721fe258",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 17.246875000000003, 'Accuracy')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "ms.validation_curve(xgb.XGBClassifier(), X_train, y_train,\n",
    "    scoring='accuracy', param_name='learning_rate',\n",
    "    param_range=[0.001, .01, .05, .1, .2, .5, .9, 1], ax=ax)\n",
    "ax.set_xlabel('Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dba35931-4dee-4f0c-8a20-11ebbed95118",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 17.246875000000003, 'roc_auc')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 7))\n",
    "ms.validation_curve(xgb.XGBClassifier(), X_train, y_train,\n",
    "    scoring='roc_auc', param_name='learning_rate',\n",
    "    param_range=[0.001, .01, .05, .1, .2, .5, .9, 1], ax=ax\n",
    ")\n",
    "ax.set_xlabel('roc_auc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2da240e8-944b-443b-b171-b0bc46b30f70",
   "metadata": {},
   "source": [
    "## 16.3 Step-wise Recall Tuning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4d3f8db6-d719-402a-8d1d-373d19c7a78c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from hyperopt import hp, Trials, fmin, tpe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "563c37da-3600-4e46-86dd-1f35487126c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████| 40/40 [00:05<00:00,  6.74trial/s, best loss: -0.7682260114069565]\n",
      "100%|███████████████████████████████████████████████| 40/40 [00:06<00:00,  5.86trial/s, best loss: -0.7688392091964893]\n",
      "100%|███████████████████████████████████████████████| 40/40 [00:06<00:00,  5.80trial/s, best loss: -0.7704547021681099]\n",
      "100%|███████████████████████████████████████████████| 40/40 [00:09<00:00,  4.35trial/s, best loss: -0.7589320015366885]\n"
     ]
    }
   ],
   "source": [
    "params = {'random_state': 42}\n",
    "\n",
    "rounds = [{'max_depth': hp.quniform('max_depth', 1, 9, 1), # tree\n",
    "    'min_child_weight': hp.loguniform('min_child_weight', -2, 3)},\n",
    "    {'subsample': hp.uniform('subsample', 0.5, 1), # stochastic\n",
    "    'colsample_bytree': hp.uniform('colsample_bytree', 0.5, 1)},\n",
    "    {'gamma': hp.loguniform('gamma', -10, 10)}, # regularization\n",
    "    {'learning_rate': hp.loguniform('learning_rate', -7, 0)} # boosting\n",
    "    ]\n",
    "for round in rounds:\n",
    "    params = {**params, **round}\n",
    "    trials = Trials()\n",
    "    best = fmin(fn=lambda space: hyperparameter_tuning(\n",
    "        space, X_train, y_train, X_test, y_test, metric=roc_auc_score),\n",
    "        space=params,\n",
    "        algo=tpe.suggest,\n",
    "        max_evals=40,\n",
    "        trials=trials,\n",
    "        )\n",
    "    \n",
    "params = {**params, **best}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "141fb023-8da5-4071-aec3-92206790df12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7451313573096131"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_def = xgb.XGBClassifier()\n",
    "xgb_def.fit(X_train, y_train)\n",
    "metrics.roc_auc_score(y_test, xgb_def.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e966b0e-ea06-4648-9936-f723bc44f663",
   "metadata": {},
   "source": [
    "The training above gives the following parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "91c2c5ce-8300-4654-8236-13c120b17dfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'random_state': 42,\n",
       " 'max_depth': 7,\n",
       " 'min_child_weight': 2.3567517599965226,\n",
       " 'subsample': 0.7096452549552429,\n",
       " 'colsample_bytree': 0.5972135962796586,\n",
       " 'gamma': 0.00038220212038342827,\n",
       " 'learning_rate': 0.015416582685690093}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params['max_depth'] = int(params['max_depth'])\n",
    "params"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd76c0c5-6ada-47a6-86f7-350798696380",
   "metadata": {},
   "source": [
    "Below I use the `'verbose=100'` parameter to only show the output every 100 iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "81a56103-42ba-4f2d-8f87-c4d5593bd68f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-logloss:0.68985\tvalidation_1-logloss:0.69037\n",
      "[100]\tvalidation_0-logloss:0.52259\tvalidation_1-logloss:0.54020\n",
      "[200]\tvalidation_0-logloss:0.47459\tvalidation_1-logloss:0.50970\n",
      "[300]\tvalidation_0-logloss:0.44892\tvalidation_1-logloss:0.49936\n",
      "[400]\tvalidation_0-logloss:0.43086\tvalidation_1-logloss:0.49586\n",
      "[442]\tvalidation_0-logloss:0.42484\tvalidation_1-logloss:0.49625\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1,\n",
       "              colsample_bytree=0.5972135962796586, early_stopping_rounds=50,\n",
       "              enable_categorical=False, eval_metric=None,\n",
       "              gamma=0.00038220212038342827, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "              importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "              learning_rate=0.015416582685690093, max_bin=256,\n",
       "              max_cat_to_onehot=4, max_delta_step=0, max_depth=7, max_leaves=0,\n",
       "              min_child_weight=2.3567517599965226, missing=nan,\n",
       "              monotone_constraints=&#x27;()&#x27;, n_estimators=500, n_jobs=0,\n",
       "              num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=42,\n",
       "              reg_alpha=0, reg_lambda=1, ...)</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=0.5, booster=&#x27;gbtree&#x27;, callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1,\n",
       "              colsample_bytree=0.5972135962796586, early_stopping_rounds=50,\n",
       "              enable_categorical=False, eval_metric=None,\n",
       "              gamma=0.00038220212038342827, gpu_id=-1, grow_policy=&#x27;depthwise&#x27;,\n",
       "              importance_type=None, interaction_constraints=&#x27;&#x27;,\n",
       "              learning_rate=0.015416582685690093, max_bin=256,\n",
       "              max_cat_to_onehot=4, max_delta_step=0, max_depth=7, max_leaves=0,\n",
       "              min_child_weight=2.3567517599965226, missing=nan,\n",
       "              monotone_constraints=&#x27;()&#x27;, n_estimators=500, n_jobs=0,\n",
       "              num_parallel_tree=1, predictor=&#x27;auto&#x27;, random_state=42,\n",
       "              reg_alpha=0, reg_lambda=1, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', callbacks=None,\n",
       "              colsample_bylevel=1, colsample_bynode=1,\n",
       "              colsample_bytree=0.5972135962796586, early_stopping_rounds=50,\n",
       "              enable_categorical=False, eval_metric=None,\n",
       "              gamma=0.00038220212038342827, gpu_id=-1, grow_policy='depthwise',\n",
       "              importance_type=None, interaction_constraints='',\n",
       "              learning_rate=0.015416582685690093, max_bin=256,\n",
       "              max_cat_to_onehot=4, max_delta_step=0, max_depth=7, max_leaves=0,\n",
       "              min_child_weight=2.3567517599965226, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=500, n_jobs=0,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=42,\n",
       "              reg_alpha=0, reg_lambda=1, ...)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_tuned = xgb.XGBClassifier(**params, early_stopping_rounds=50,\n",
    "  n_estimators=500)\n",
    "\n",
    "xgb_tuned.fit(X_train, y_train, eval_set=[(X_train, y_train),\n",
    " (X_test, y_test)], verbose=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2f526f66-243d-4175-b49e-672d13fedb77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7582892520464551"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.roc_auc_score(y_test, xgb_tuned.predict(X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58cf385d-e831-499b-88bc-83bebd1df751",
   "metadata": {},
   "source": [
    "I love this improvement.\n",
    "\n",
    "## Summary\n",
    "\n",
    "- The purpose of a validation curve is to determine training and test scores for varying parameter values. It computes scores for an estimator with different values of a specified parameter. This is similar to grid search with one parameter. However, this will also compute training scores and is merely a utility for plotting the results.\n",
    "- Tuning a model for recall differs from tuning for accuracy because recall and accuracy are different metrics that measure different aspects of a model's performance. Recall measures the proportion of true positive instances that were correctly identified by the model, while accuracy measures the proportion of all instances that were correctly classified. As a result, optimizing for one metric may not necessarily optimize for the other.\n",
    "- The choice of metric can influence a model's hyperparameters because different metrics may require different hyperparameter settings to achieve optimal performance. For example, if you are optimizing for recall, you may need to adjust the hyperparameters in a way that increases the true positive rate, even if it means sacrificing some precision.\n",
    "- It is possible to optimize a model for both precision and recall simultaneously, but there is often a trade-off between the two. This trade-off can be visualized using a precision-recall curve, which shows how precision and recall change as the decision threshold is varied. The goal is to find a balance between precision and recall that achieves good performance on both metrics.\n",
    "- It might be important to prioritize recall over precision or vice versa depending on the specific problem you are trying to solve. For example, in a medical diagnosis scenario where failing to identify a disease could have serious consequences, it may be more important to prioritize recall (i.e., correctly identifying all positive cases) even if it means sacrificing some precision (i.e., having more false positives). On the other hand, in a spam email classification scenario where incorrectly classifying an email as spam could result in lost business opportunities, it may be more important to prioritize precision (i.e., minimizing false positives) even if it means sacrificing some recall (i.e., having more false negatives).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "997929f9-348d-4dc5-8863-b28a503fda5b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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