CODEWITHSUNDEEP
catboost
Sebastian Kozik
Sebastian Kozik

Reputation: 91

CatBoostClassifier - AUC metric

I got question about CatBoostClassifier.

params = {
'loss_function' : 'Logloss',
'eval_metric' : 'AUC',
'verbose' : 200,
'random_seed' : 42,
'custom_metric' : 'AUC:hints=skip_train~false'
}

cbc = CatBoostClassifier(**params)
cbc.fit(x_tr, y_tr,
        eval_set = (x_te, y_te),
        use_best_model = True,
        plot = True
        );

predictions = cbc.predict(x_te)

Model results:

bestTest = 0.6786987522

But when I try :

 from sklearn import metrics
 auc = metrics.roc_auc_score(y_te, predictions)
 auc

I got 0.5631684491978609 result. Why this results differ ? What first and second result mean ? Which one is final metric of my cbc model ?

Upvotes: 0

Views: 6375

Answers (1)

Sebastian Kozik
Sebastian Kozik

Reputation: 91

OK, I found solution. I should use:

predictions = cbc.predict_proba(x_te)

rather than

predictions = cbc.predict(x_te)

Now I have the same results.

Upvotes: 6

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