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?
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