sklearn kfold
from sklearn.model_selection import GridSearchCV from sklearn.model_selection import KFold # Regressor lrg = LinearRegression() #Param Grid param_grid=[{ 'normalize':[True, False] }] # Grid Search with KFold, not shuffled in this example experiment_gscv = GridSearchCV(lrg, param_grid, cv=KFold(n_splits=4, shuffle=False), scoring='neg_mean_squared_error')
cross validation
Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross-validation.
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_mat
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