You can use sklearn's cross_val_score() like-
cross_val_score(estimator, predictors_data, target_variable, scoring = None, cv = None)
where
estimator is our ML model,
scoring is the evaluation metric that we specify (Refer to metrics for seeing a list of available metrics in sklearn for the scoring parameter) and
cv is the value of k in the k-fold.
We use neg_root_mean_squared_error as the scoring metric for our task. It is negative RMSE. Sklearn's cross-validation features uses a utility function instead of a cost function. In the cost function, the cost will be lower for a better model while in a utility function, it should be greater for a better model.
And because of this convention of sklearn to use a utility function while cross validating, we use the scoring function as negative RMSE. It is the opposite of the RMSE. So, negative RMSE is just a negative version of the numbers which we get in RMSE. So, if for one data point, RMSE comes as 3 then negative RMSE will be -3 for that.
Refer to cross_val_score documentation for further details about the method.
Import function cross_val_score from sklearn.model_selection.
Use the cross_val_score function and provide tree_reg as estimator, housing_prepared and housing_labels as predictors_data and target_variable, neg_root_mean_squared_error as the scoring metric and cv as 10 for parameters as we want to perform 10-fold cross-validation. Store the output in a variable named scores.
The scores will be negative. Pass them through abs() function to convert them in positives by-
scores = abs(scores)
Note- Scores will be different on different runs due to the stochastic nature of the function.
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