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lab ensemble #3

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1d01e3c
lab ensemble
danisiaj Oct 14, 2024
3f66a78
ensemble corrected
danisiaj Oct 14, 2024
d26b8c3
Update main.ipynb
danisiaj Oct 14, 2024
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247 changes: 226 additions & 21 deletions your-code/main.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
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"execution_count": 43,
"id": "00cf591d-8a5b-499e-8715-1ad140867934",
"metadata": {},
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Expand All @@ -28,10 +28,158 @@
},
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"execution_count": null,
"execution_count": 44,
"id": "0bb5ea1c-a4e5-4419-bae8-661fe2d82711",
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"outputs": [
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"text/plain": [
" age sex cp trestbps chol fbs restecg thalach exang oldpeak slope \\\n",
"0 63 1 3 145 233 1 0 150 0 2.3 0 \n",
"1 37 1 2 130 250 0 1 187 0 3.5 0 \n",
"2 41 0 1 130 204 0 0 172 0 1.4 2 \n",
"3 56 1 1 120 236 0 1 178 0 0.8 2 \n",
"4 57 0 0 120 354 0 1 163 1 0.6 2 \n",
"\n",
" ca thal target \n",
"0 0 1 1 \n",
"1 0 2 1 \n",
"2 0 2 1 \n",
"3 0 2 1 \n",
"4 0 2 1 "
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
Expand All @@ -46,7 +194,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 45,
"id": "23ad7e40-87f3-4b93-bef9-a9ddb5881ddc",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -75,23 +223,39 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 46,
"id": "d39376f1-b4ca-44c0-8364-d11b9a7605f9",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Accuracy: 0.7894736842105263\n",
"Train Accuracy: 1.0\n"
]
}
],
"source": [
"#Create and Train a Decision Tree Classifier and print the train and test accuracy\n",
"\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import accuracy_score, mean_squared_error\n",
"\n",
"# Train Decision Tree\n",
"\n",
"tree_model = DecisionTreeClassifier()\n",
"tree_model.fit(X_train_scaled, y_train)\n",
"\n",
"# Predictions and evaluation\n",
"y_pred_tree_test = tree_model.predict(X_test_scaled)\n",
"y_pred_tree_train = tree_model.predict(X_train_scaled)\n",
"\n",
"# Evaluation and sores\n",
"accuracy_tree_test = accuracy_score(y_test, y_pred_tree_test)\n",
"accuracy_tree_train = accuracy_score(y_train, y_pred_tree_train)\n",
"\n",
"# Evaluate performance\n"
"print(f\"Test Accuracy: {accuracy_tree_test}\")\n",
"print(f\"Train Accuracy: {accuracy_tree_train}\")"
]
},
{
Expand All @@ -104,7 +268,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 47,
"id": "9c60160a-b179-4896-a026-4beab803bb4e",
"metadata": {},
"outputs": [],
Expand Down Expand Up @@ -135,10 +299,19 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 48,
"id": "8fc76766-a90c-47ed-bd02-66827a1dc115",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Accuracy: 0.8157894736842105\n",
"Train Accuracy: 0.9911894273127754\n"
]
}
],
"source": [
"# Create and Train a BaggingClassifier. \n",
"# Use as base estimator a weak decision tree (max_depth=1) and 100 estimators to really over a lot of different data samples\n",
Expand All @@ -147,12 +320,19 @@
"from sklearn.ensemble import BaggingClassifier\n",
"\n",
"# Train BaggingClassifier\n",
"\n",
"bag_model = BaggingClassifier()\n",
"bag_model.fit(X_train_scaled, y_train)\n",
"\n",
"# Predictions and evaluation\n",
"y_pred_bag_test = bag_model.predict(X_test_scaled)\n",
"y_pred_bag_train = bag_model.predict(X_train_scaled)\n",
"\n",
"# Evaluation and sores\n",
"accuracy_bag_test = accuracy_score(y_test, y_pred_bag_test)\n",
"accuracy_bag_train = accuracy_score(y_train, y_pred_bag_train)\n",
"\n",
"# Evaluate performance\n"
"print(f\"Test Accuracy: {accuracy_bag_test}\")\n",
"print(f\"Train Accuracy: {accuracy_bag_train}\")"
]
},
{
Expand All @@ -165,7 +345,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 49,
"id": "9f892484-618a-46fe-8e56-0a18fa652ed8",
"metadata": {},
"outputs": [],
Expand All @@ -192,10 +372,27 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 50,
"id": "4bba1773-b0b0-44ba-a838-58b8c466ff88",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Test Accuracy: 0.8157894736842105\n",
"Train Accuracy: 0.9911894273127754\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"c:\\Users\\danis\\anaconda3\\Lib\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:519: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
" warnings.warn(\n"
]
}
],
"source": [
"# Create and Train a AdaBoostClassifier. \n",
"# Use as base estimator a weak decision tree (max_depth=1) and 100 estimators to really target the specific behaviors of this phenomenon\n",
Expand All @@ -204,12 +401,20 @@
"from sklearn.ensemble import AdaBoostClassifier\n",
"\n",
"# Train AdaBoost\n",
"\n",
"ada_model = AdaBoostClassifier()\n",
"ada_model.fit(X_train_scaled, y_train)\n",
"\n",
"# Predictions and evaluation\n",
"y_pred_ada_test = ada_model.predict(X_test_scaled)\n",
"y_pred_ada_train = ada_model.predict(X_train_scaled)\n",
"\n",
"# Evaluate performance\n",
"\n",
"accuracy_ada_test = accuracy_score(y_test, y_pred_ada_test)\n",
"accuracy_ada_train = accuracy_score(y_train, y_pred_ada_train)\n",
"\n",
"# Evaluate performance\n"
"print(f\"Test Accuracy: {accuracy_ada_test}\")\n",
"print(f\"Train Accuracy: {accuracy_ada_train}\")\n"
]
},
{
Expand All @@ -222,7 +427,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 51,
"id": "4b5e21fe-0a8f-45f6-a2d3-74261941f9c1",
"metadata": {},
"outputs": [],
Expand All @@ -235,7 +440,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"display_name": "base",
"language": "python",
"name": "python3"
},
Expand All @@ -249,7 +454,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.2"
"version": "3.12.4"
}
},
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Expand Down