KNN_best_model,KNN_best_params = KNN_RandSearch.RandomSearch() # Run the best model pipeline models = f.GetScaledModelwithbestparams('standard', LR_best_params, Ad_best_params, GB_best_params, RF_best_params, CART_best_params, KNN_best_params) names, results = f.cv_score(X_train,y_train,models,['accuracy', 'specificity_cl_1', 'precision_cl_1', 'recall_cl_1', 'specificity_cl_2', 'precision_cl_2', 'recall_cl_2', 'AUC_cl_1', 'AUC_cl_2', 'MCC']) ScoreCard = f.concat_model_score(names, results, ScoreCard) results_df = pd.DataFrame(results, index = names) # HYPER PARAMETER TUNING WITH GRID SEARCH for best performing models # Logistic Regression model penalty = ['l1', 'l2']
def scaled_model_with_CW_factor(self, scoring, SEED, scalar): models = f.GetScaledModelwithfactorizedCW(scalar) names, results = f.cv_score(self.X_train, self.y_train, models, scoring, SEED) _score = f.cv_metrics(names, results) return _score