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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