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_matCopyright © 2021 Codeinu
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