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
