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cross validation

Shyamlal padmanabhan 
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.
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4.29
7
Kafeaulait 135 points 

                                    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')

4.29 (7 Votes)
0
4.33
3
Alex 65 points 

                                    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

4.33 (3 Votes)
0
4.2
5
Ashraff 110 points 

                                    ModuleNotFoundError: No module named 'sklearn.cross_validation'

4.2 (5 Votes)
0
4
1
Man of Letters 125 points 

                                    cross validation error

4 (1 Votes)
0
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