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GridSearchCV.best_score_ meaning when scoring set to 'accuracy' and CV

I'm trying to find the best model Neural Network model applied for the classification of breast cancer samples on the well-known Wisconsin Cancer dataset (569 samples, 31 features + target). I'm using sklearn 0.18.1. I'm not using Normalization so far. I'll add it when I solve this question.

# some init code omitted
X_train, X_test, y_train, y_test = train_test_split(X, y)

Define params NN params for the GridSearchCV

tuned_params = [{'solver': ['sgd'], 'learning_rate': ['constant'], "learning_rate_init" : [0.001, 0.01, 0.05, 0.1]},
                {"learning_rate_init" : [0.001, 0.01, 0.05, 0.1]}]

CV method and model

cv_method = KFold(n_splits=4, shuffle=True)
model = MLPClassifier()

Apply grid

grid = GridSearchCV(estimator=model, param_grid=tuned_params, cv=cv_method, scoring='accuracy')
grid.fit(X_train, y_train)
y_pred = grid.predict(X_test)

And if I run:

print(grid.best_score_)
print(accuracy_score(y_test, y_pred))

The result is 0.746478873239 and 0.902097902098

According to the doc "best_score_ : float, Score of best_estimator on the left out data". I assume it is the best accuracy among the ones obtained running the 8 different configuration as especified in tuned_params the number of times especified by KFold, on the left out data as especified by KFold. Am I right?

One more question. Is there a method to find the optimal size of test data to use in train_test_split which defaults to 0.25?

Thanks a lot

REFERENCES

  • http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
  • http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html#sklearn.model_selection.GridSearchCV
  • http://scikit-learn.org/stable/modules/grid_search.html
  • http://scikit-learn.org/stable/modules/cross_validation.html
  • http://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py
like image 537
Taka Avatar asked Jun 09 '17 14:06

Taka


1 Answers

The grid.best_score_ is the average of all cv folds for a single combination of the parameters you specify in the tuned_params.

In order to access other relevant details about the grid searching process, you can look at the grid.cv_results_ attribute.

From the documentation of GridSearchCV:

cv_results_ : dict of numpy (masked) ndarrays

A dict with keys as column headers and values as columns, 
that can be imported into a pandas DataFrame

It contains keys like 'split0_test_score', 'split1_test_score' , 'mean_test_score', 'std_test_score', 'rank_test_score', 'split0_train_score', 'split1_train_score', 'mean_train_score', etc, which gives additional information about the whole execution.

like image 50
Vivek Kumar Avatar answered Sep 22 '22 11:09

Vivek Kumar