Evaluate multiple scores on sklearn cross_val_score

Question

I\'m trying to evaluate multiple machine learning algorithms with sklearn for a couple of metrics (accuracy, recall, precision and maybe more).

For what I understood

Answer 1

from sklearn import model_selection

def error_metrics(model, train_data, train_targ, kfold):
    scoring = ["accuracy","roc_auc","neg_log_loss","r2",
             "neg_mean_squared_error","neg_mean_absolute_error"] 

    error_metrics = pd.DataFrame()
    error_metrics["model"] = model
    for scor in scoring:
        score = []
        for mod in model:
           
            result = model_selection.cross_val_score(estimator= mod, X=train_data, y=train_targ,cv=kfold,scoring=scor )
            score.append(result.mean())
            
        error_metrics[scor] =pd.Series(score)
        
    return error_metrics