Interpreting sklearns' GridSearchCV best score

Question

I would like to know the difference between the score returned by GridSearchCV and the R2 metric calculated as below. In other cases I receive the grid search score highly negative (same applies for cross_val_score) and I would be grateful for explaining what it is.

from sklearn import datasets
from sklearn.model_selection import (cross_val_score, GridSearchCV)
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import accuracy_score, r2_score
from sklearn import tree

diabetes = datasets.load_diabetes()
X = diabetes.data[:150]
y = diabetes.target[:150]
X = pd.DataFrame(X)

parameters = {'splitter':('best','random'), 
              'max_depth':np.arange(1,10), 
              'min_samples_split':np.arange(2,10), 
              'min_samples_leaf':np.arange(1,5)}

regressor = GridSearchCV(DecisionTreeRegressor(), parameters, scoring = 'r2', cv = 5)
regressor.fit(X, y)

print('Best score: ', regressor.best_score_)
best = regressor.best_estimator_
print('R2: ', r2_score(y_pred = best.predict(X), y_true = y))

Answer 1

The regressor.best_score_ is the average of r2 scores on left-out test folds for the best parameter combination.

In your example, the cv=5, so the data will be split into train and test folds 5 times. The model will be fitted on train and scored on test. These 5 test scores are averaged to get the score. Please see documentation:

"best_score_: Mean cross-validated score of the best_estimator"

The above process repeats for all parameter combinations. And the best average score from it is assigned to the best_score_.

You can look at my other answer for complete working of GridSearchCV

After finding the best parameters, the model is trained on full data.

r2_score(y_pred = best.predict(X), y_true = y)

is on the same data as the model is trained on, so in most cases, it will be higher.