model

model.py

parameters = {'max_depth':[1, 5, 10, 20, 50],
              'min_samples_split':[5, 10, 100, 500]}

dt = DecisionTreeClassifier()

dt_grid = GridSearchCV(dt, param_grid=parameters, n_jobs=-1, verbose=1,scoring='f1_macro',cv=3,return_train_score=True)
dt_grid.fit(X_train,y_train)

best_depth=dt_grid.best_params_['max_depth']
best_samples=dt_grid.best_params_['min_samples_split']
dt_1 = DecisionTreeClassifier(max_depth=best_depth,min_samples_split=best_samples)

dt_1.fit(X_train, y_train)