Why when I use GridSearchCV with roc_auc scoring,

Question

I am using stratified 10-fold cross validation to find model that predicts y (binary outcome) from X (X has 34 labels) with the highest auc. I set the GridSearchCV:

log_reg = LogisticRegression()
parameter_grid = {'penalty' : ["l1", "l2"],'C': np.arange(0.1, 3, 0.1),}
cross_validation = StratifiedKFold(n_splits=10,shuffle=True,random_state=100)
grid_search = GridSearchCV(log_reg, param_grid = parameter_grid,scoring='roc_auc',
                          cv = cross_validation)

And then do the cross-validation:

grid_search.fit(X, y)
y_pr=grid_search.predict(X)

I do not understand the following: why grid_search.score(X,y) and roc_auc_score(y, y_pr) give different results (the former is 0.74 and the latter is 0.63)? Why do not these commands do the same thing in my case?

Answer 1

This is due to different initialization of roc_auc when used in GridSearchCV.

Look at the source code here

roc_auc_scorer = make_scorer(roc_auc_score, greater_is_better=True,
                             needs_threshold=True)

Observe the third parameter needs_threshold. When true, it will require the continous values for y_pred such as probabilities or confidence scores which in gridsearch will be calculated from log_reg.decision_function().

When you explicitly call roc_auc_score with y_pr, you are using .predict() which will output the resultant predicted class labels of the data and not probabilities. That should account for the difference.

Try :

y_pr=grid_search.decision_function(X)
roc_auc_score(y, y_pr)

If still not same results, please update the question with complete code and some sample data.