sklearn cross validation score
from sklearn.model_selection import cross_val_score
scores = cross_val_score(classifier_logreg, X_train, y_train, cv = 5, scoring='accuracy')
print('Cross-validation scores:{}'.format(scores))
print('Average cross-validation score: {}'.format(scores.mean()))classification cross validation
from sklearn.model_selection import cross_val_predict
xgb=XGBClassifier(colsample_bytree=0.8, learning_rate=0.4, max_depth=4)
cvs=cross_val_score(xgb,x,y,scoring='accuracy',cv=10)
print('cross_val_scores= ',cvs.mean())
y_pred=cross_val_predict(xgb,x,y,cv=10)
conf_mat=confusion_matrix(y_pred,y)
conf_matCopyright © 2021 Codeinu
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