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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])
View pycaret_boosting.py
# ensemble boosting
boosting = classification.ensemble_model(classification_dt, method= 'Boosting')
View pycaret_5.py
# build and tune the catboost model
tune_catboost = classification.tune_model('catboost')
View pycaret_4.py
# build the xgboost model
classification_xgb = classification.create_model('xgboost')
View pycaret_3.py
# build the decision tree model
classification_dt = classification.create_model('dt')
View pycaret_2.py
# import the classification module
from pycaret import classification
# setup the environment
classification_setup = classification.setup(data= data_classification, target='Personal Loan')