[scikit-learn] GridsearchCV returns worse scoring the broader parameter space gets

[Andreas Tosstorff]

Dear all,

I am new to scikit learn so please excuse my ignorance. Using GridsearchCV I am trying to optimize a DecisionTreeRegressor. The broader I make the parameter space, the worse the scoring gets.

Setting min_samples_split to range(2,10) gives me a neg_mean_squared_error of -0.04. When setting it to range(2,5) The score is -0.004.

simple_tree =GridSearchCV(tree.DecisionTreeRegressor(random_state=42), n_jobs=4, param_grid={'min_samples_split': range(2, 10)}, scoring='neg_mean_squared_error', cv=10, refit='neg_mean_squared_error')

simple_tree.fit(x_tr,y_tr).score(x_tr,y_tr)


I expect an equal or more positive score for a more extensive grid search compared to the less extensive one.

I would really appreciate your help!

Kind regards,
Andreas
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