Ran final model
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Petrus committed
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1 file changed
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@@ -11,7 +11,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"id": "0952f099",
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"metadata": {},
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"outputs": [
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@@ -205,7 +205,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 3,
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"id": "e68b1ea9",
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"metadata": {},
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"outputs": [],
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@@ -401,62 +401,10 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 5,
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"id": "4185a428",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Tuning Decision Tree ...\n",
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"Tuning Random Forest ...\n",
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"Tuning XGBoost ...\n",
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"\n",
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"------ Results Sorted by Accuracy ------\n",
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" Model Accuracy Precision Recall F1 Score Best Params Training Time (s) Prediction Time (s) Total Time (s)\n",
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" XGBoost 0.867064 0.862867 0.867064 0.863685 {'learning_rate': 0.25, 'max_depth': 6, 'n_estimators': 75} 26.702878 0.014437 26.717316\n",
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"Random Forest 0.858942 0.853479 0.858942 0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100} 158.303091 0.076534 158.379625\n",
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"Decision Tree 0.844356 0.838363 0.844356 0.839763 {'max_depth': 12, 'min_samples_split': 6} 2.376242 0.002706 2.378948\n",
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"\n",
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"------ Results Sorted by Total Time ------\n",
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" Model Accuracy Precision Recall F1 Score Best Params Training Time (s) Prediction Time (s) Total Time (s)\n",
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"Decision Tree 0.844356 0.838363 0.844356 0.839763 {'max_depth': 12, 'min_samples_split': 6} 2.376242 0.002706 2.378948\n",
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" XGBoost 0.867064 0.862867 0.867064 0.863685 {'learning_rate': 0.25, 'max_depth': 6, 'n_estimators': 75} 26.702878 0.014437 26.717316\n",
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"Random Forest 0.858942 0.853479 0.858942 0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100} 158.303091 0.076534 158.379625\n"
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]
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},
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{
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"data": {
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"image/png": 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truncated
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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Define the classifiers and their hyperparameters in a finer grid\n",
|
||||
"def get_fine_grids(use_bootstrap=True):\n",
|
||||
@@ -478,13 +426,13 @@
|
||||
" }\n",
|
||||
"\n",
|
||||
"# optimized classifiers and find their hyperparameters\n",
|
||||
"clf_opt = ClassifierComparisonOpt(X_train_val, y_train_val)\n",
|
||||
"#clf_opt = ClassifierComparisonOpt(X_train_val, y_train_val)\n",
|
||||
"fine_grid = get_fine_grids()\n",
|
||||
"clf_opt.fit_models(fine_grid)\n",
|
||||
"clf_opt.print_summary()\n",
|
||||
"#clf_opt.fit_models(fine_grid)\n",
|
||||
"#clf_opt.print_summary()\n",
|
||||
"\n",
|
||||
"# Show feature importance for each model\n",
|
||||
"clf_opt.show_feature_importance()"
|
||||
"#clf_opt.show_feature_importance()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -498,23 +446,19 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 4,
|
||||
"id": "776cdb17",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Tuning Decision Tree ...\n",
|
||||
"Tuning XGBoost ...\n",
|
||||
"Tuning Random Forest ...\n",
|
||||
"\n",
|
||||
"------ Results Sorted by Accuracy ------\n",
|
||||
" Model Accuracy Precision Recall F1 Score Best Params Training Time (s) Prediction Time (s) Total Time (s)\n",
|
||||
" XGBoost 0.866899 0.862456 0.866899 0.863047 {'learning_rate': 0.2, 'max_depth': 6, 'n_estimators': 75} 25.690541 0.011451 25.701992\n",
|
||||
"Random Forest 0.858942 0.853479 0.858942 0.853443 {'max_depth': 20, 'min_samples_split': 10, 'n_estimators': 100} 140.522713 0.048441 140.571154\n",
|
||||
"Decision Tree 0.847008 0.840347 0.847008 0.840760 {'max_depth': 10, 'min_samples_split': 4} 11.239021 0.005791 11.244812\n"
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'fine_grid' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||||
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
|
||||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m 8\u001b[39m \u001b[38;5;66;03m# Train decision tree without redundant features\u001b[39;00m\n\u001b[32m 9\u001b[39m cmp_dt = ClassifierComparisonOpt(X_dt, y_train_val)\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m cmp_dt.fit_models({\u001b[33m'\u001b[39m\u001b[33mDecision Tree\u001b[39m\u001b[33m'\u001b[39m: \u001b[43mfine_grid\u001b[49m[\u001b[33m'\u001b[39m\u001b[33mDecision Tree\u001b[39m\u001b[33m'\u001b[39m]})\n\u001b[32m 12\u001b[39m \u001b[38;5;66;03m# Train XGBoost without redundant features\u001b[39;00m\n\u001b[32m 13\u001b[39m cmp_xgb = ClassifierComparisonOpt(X_xgb, y_train_val)\n",
|
||||
"\u001b[31mNameError\u001b[39m: name 'fine_grid' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -556,10 +500,63 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 8,
|
||||
"id": "a470fd20",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Classification Report:\n",
|
||||
" precision recall f1-score support\n",
|
||||
"\n",
|
||||
" Poor 0.87 0.95 0.90 4533\n",
|
||||
" Rich 0.77 0.56 0.65 1500\n",
|
||||
"\n",
|
||||
" accuracy 0.85 6033\n",
|
||||
" macro avg 0.82 0.75 0.78 6033\n",
|
||||
"weighted avg 0.84 0.85 0.84 6033\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 640x480 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Classification Report:\n",
|
||||
" precision recall f1-score support\n",
|
||||
"\n",
|
||||
" Poor 0.89 0.94 0.91 4533\n",
|
||||
" Rich 0.77 0.63 0.70 1500\n",
|
||||
"\n",
|
||||
" accuracy 0.86 6033\n",
|
||||
" macro avg 0.83 0.79 0.80 6033\n",
|
||||
"weighted avg 0.86 0.86 0.86 6033\n",
|
||||
"\n",
|
||||
"Classification Report:\n",
|
||||
" precision recall f1-score support\n",
|
||||
"\n",
|
||||
" Poor 0.89 0.94 0.91 4533\n",
|
||||
" Rich 0.77 0.66 0.71 1500\n",
|
||||
"\n",
|
||||
" accuracy 0.87 6033\n",
|
||||
" macro avg 0.83 0.80 0.81 6033\n",
|
||||
"weighted avg 0.86 0.87 0.86 6033\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# ---------------------------- Decision Tree ----------------------------\n",
|
||||
"dt = Pipeline([\n",
|
||||
@@ -574,6 +571,12 @@
|
||||
"print(\"Classification Report:\")\n",
|
||||
"print(classification_report(y_test, y_pred_dt, target_names=[\"Poor\", \"Rich\"]))\n",
|
||||
"\n",
|
||||
"CM = confusion_matrix(y_test, y_pred_dt)\n",
|
||||
"disp = ConfusionMatrixDisplay(confusion_matrix=CM, display_labels=[\"Poor\", \"Rich\"])\n",
|
||||
"disp.plot(cmap=\"Blues\")\n",
|
||||
"plt.title(\"Test Confusion Matrix (dt)\")\n",
|
||||
"plt.show() \n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ---------------------------- Random Forest ----------------------------\n",
|
||||
"rf = Pipeline([\n",
|
||||
@@ -582,11 +585,11 @@
|
||||
"\n",
|
||||
"# Train the model\n",
|
||||
"rf.fit(X_train_val, y_train_val)\n",
|
||||
"y_pred = rf.predict(X_test)\n",
|
||||
"y_pred_rf = rf.predict(X_test)\n",
|
||||
"\n",
|
||||
"# Classification Report\n",
|
||||
"print(\"Classification Report:\")\n",
|
||||
"print(classification_report(y_test, y_pred_dt, target_names=[\"Poor\", \"Rich\"]))\n",
|
||||
"print(classification_report(y_test, y_pred_rf, target_names=[\"Poor\", \"Rich\"]))\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# ---------------------------- XGBoost ----------------------------\n",
|
||||
@@ -596,11 +599,11 @@
|
||||
"\n",
|
||||
"# Train the model\n",
|
||||
"xgb.fit(X_train_val, y_train_val)\n",
|
||||
"y_pred = xgb.predict(X_test)\n",
|
||||
"y_pred_xgb = xgb.predict(X_test)\n",
|
||||
"\n",
|
||||
"# Classification Report\n",
|
||||
"print(\"Classification Report:\")\n",
|
||||
"print(classification_report(y_test, y_pred_dt, target_names=[\"Poor\", \"Rich\"]))"
|
||||
"print(classification_report(y_test, y_pred_xgb, target_names=[\"Poor\", \"Rich\"]))"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Reference in new issue
Block a user