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Lakshay lakshay-arora

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View pycaret_save_model.py
# save the model
classification.save_model(classification_dt, 'decision_tree_1')
View prediction_test_pycaret.py
# read the test data
test_data_classification = pd.read_csv('datasets/loan_test_data.csv')
# make predictions
predictions = classification.predict_model(classification_dt, data=test_data_classification)
# view the predictions
predictions
View interpret_model_2.py
# interpret model : Correlation
classification.interpret_model(classification_xgb,plot='correlation')
View interpret_model.py
# interpret_model: SHAP
classification.interpret_model(classification_xgb)
View evaluate_model.py
# evaluate model
classification.evaluate_model(classification_dt)
View plot_3.py
# Dimension Learning
classification.plot_model(classification_dt, plot = 'feature')
# Confusion Matrix
classification.plot_model(classification_dt, plot = 'confusion_matrix')
View plot_2.py
# Precision Recall Curve
classification.plot_model(classification_dt, plot = 'pr')
# Validation Curve
classification.plot_model(classification_dt, plot = 'vc')
View plot_1.py
# AUC-ROC plot
classification.plot_model(classification_dt, plot = 'auc')
# Decision Boundary
classification.plot_model(classification_dt, plot = 'boundary')
View comapre_models.py
# compare performance of different classification models
classification.compare_models()
View pycaret_blender.py
# Ensemble: blending
blender = classification.blend_models(estimator_list=[classification_dt, classification_xgb])
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