Hello I'm doing a GridSearchCV and I'm printing the result with the .cv_results_ function from scikit learn.
My problem is that when I'm evaluating by hand the mean on all the test score splits I obtain a different number compared to what it is written in 'mean_test_score'. Which is different from the standard np.mean()?
I attach here the code with the result:
n_estimators = [100]
max_depth = [3]
learning_rate = [0.1]
param_grid = dict(max_depth=max_depth, n_estimators=n_estimators, learning_rate=learning_rate)
gkf = GroupKFold(n_splits=7)
grid_search = GridSearchCV(model, param_grid, scoring=score_auc, cv=gkf)
grid_result = grid_search.fit(X, Y, groups=patients)
grid_result.cv_results_
The result of this operation is:
{'mean_fit_time': array([ 8.92773601]),
'mean_score_time': array([ 0.04288721]),
'mean_test_score': array([ 0.83490629]),
'mean_train_score': array([ 0.95167036]),
'param_learning_rate': masked_array(data = [0.1],
mask = [False],
fill_value = ?),
'param_max_depth': masked_array(data = [3],
mask = [False],
fill_value = ?),
'param_n_estimators': masked_array(data = [100],
mask = [False],
fill_value = ?),
'params': ({'learning_rate': 0.1, 'max_depth': 3, 'n_estimators': 100},),
'rank_test_score': array([1]),
'split0_test_score': array([ 0.74821666]),
'split0_train_score': array([ 0.97564995]),
'split1_test_score': array([ 0.80089016]),
'split1_train_score': array([ 0.95361201]),
'split2_test_score': array([ 0.92876979]),
'split2_train_score': array([ 0.93935856]),
'split3_test_score': array([ 0.95540287]),
'split3_train_score': array([ 0.94718634]),
'split4_test_score': array([ 0.89083901]),
'split4_train_score': array([ 0.94787374]),
'split5_test_score': array([ 0.90926355]),
'split5_train_score': array([ 0.94829775]),
'split6_test_score': array([ 0.82520379]),
'split6_train_score': array([ 0.94971417]),
'std_fit_time': array([ 1.79167576]),
'std_score_time': array([ 0.02970254]),
'std_test_score': array([ 0.0809713]),
'std_train_score': array([ 0.0105566])}
As you can see, doing the np.mean of all the test_score it gives you a value approximately of 0.8655122606479532 while the 'mean_test_score' is 0.83490629
Thanks for you help, Leonardo.