Answers for "model_results = list() model_names = list() for model_name in models: model = models[model_name] k_fold = KFold(n_splits=folds, random_state=seed) results = cross_val_score(model, X_train, y_train, cv=k_fold, scoring=metric)"

Python

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')

Posted by: Guest on November-04-2020

Code answers related to "model_results = list() model_names = list() for model_name in models: model = models[model_name] k_fold = KFold(n_splits=folds, random_state=seed) results = cross_val_score(model, X_train, y_train, cv=k_fold, scoring=metric)"

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Answers for "model_results = list() model_names = list() for model_name in models: model = models[model_name] k_fold = KFold(n_splits=folds, random_state=seed) results = cross_val_score(model, X_train, y_train, cv=k_fold, scoring=metric)"

Code answers related to "model_results = list() model_names = list() for model_name in models: model = models[model_name] k_fold = KFold(n_splits=folds, random_state=seed) results = cross_val_score(model, X_train, y_train, cv=k_fold, scoring=metric)"

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