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I'm currently working on a clasification problem through random forest.

When I use GridSearchCV, using the parameter scoring="recall", the best_estimator_ is:

RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
        max_depth=5, max_features='sqrt', max_leaf_nodes=None,
        min_impurity_decrease=0.0, min_impurity_split=None,
        min_samples_leaf=5, min_samples_split=2,
        min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
        oob_score=False, random_state=700, verbose=0, warm_start=False)

and the grid.best_score_ = 0.63860

but when I save this randomforest in a variable called "m" and use:

 from sklearn.metrics import classification_report
 m_predictions = m.predict(X_train)
 print (classification_report(y_train,m_predictions, digits = 5))

the results I get are :

        precision    recall  f1-score   support

      0    0.85268   0.95728   0.90196      1826
      1    0.86644   0.62624   0.72701       808

avg/total  0.85690   0.85573   0.84829      2634

Why in the first case I get a recall of 0.63860 and in the second one of 0.85573? Shouldn't the same value be, or at least, a close value?

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  • $\begingroup$ I don't know Python so I can't really answer what is going on, but: have you checked whether these functions all return the same result? The fact that the RF model returns one recall score while the classification_report function returns two is already suspicious. Also how does this function estimate recall? CV? $\endgroup$ Jan 7 '19 at 9:20
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The GridsearchCV model performs cross-validation. Depending on your scikit version it might be 3-folds. Your actual code would be helpful. However, I am guessing that this is your problem. Note the following sentence from the documentation:

best_score_ : float

Mean cross-validated score of the best_estimator

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