When using GridSearchCV with regression tree how to interpret mean_test_score? I am using GridSearchCV to tune hyperparameters of regression decision tree. When I do, I get mean_test_score but I thought it would return mean MSE since it is a regressor. how to interpret mean_test_score? Is there a way to tweak GridSearchCV so it returns mean MSE?
here is my code
tree_reg = GridSearchCV(DecisionTreeRegressor(criterion=&quot;mse&quot;), {
    &quot;min_samples_split&quot;:[2,3,4],
    &quot;min_samples_leaf&quot;:[1,2,3]
}, cv=5, return_train_score=False)

tree_reg.fit(X, y)

pd.DataFrame(tree_reg.cv_results_)
&gt;&gt;&gt; params                   split0_test_score  .... mean_test_score
   {&quot;min_samples_leaf&quot;:2,        0.998782             0.9989933
    &quot;min_samples_split&quot;:3}

   {&quot;min_samples_leaf&quot;:2,        0.998823             0.998930
    &quot;min_samples_split&quot;:4}
            ...

what does mean_test_score mean?
By default, GridSearchCV uses the score method of its estimator; see the last paragraph of the scoring parameter on the docs:

If None, the estimator’s score method is used.

And DecisionTreeRegressor.score (indeed, all/most regressors) uses R^2.

In response to your edit: you can specify scoring='neg_mean_squared_error'.  But note too that there's a linear relationship between MSE and R^2, so optimizing either of these are equivalent.