I have a quite simple text classification setup where i need to optimize the precision score. I use scikit-learn with a LinearSVC and a TfidfVectorizer. To find the optimal parameters, i use a GridSearchCV as in the scikit-learn example.

My data set consists of 3400 text samples, from which 450 are labeled as 1. Therefore, i set the class_weight parameter of the SVM to 'auto', as is suggested in the documentation (it has been renamed to 'balanced' in the latest version of scikit).

training = load_training_data(some_file.json)

d_train = training['data']
d_test = training['target']

x_train, x_test, y_train, y_test = train_test_split(
    d_train, d_test, test_size=0.33)

vectorizer = TfidfVectorizer()

X_train = vectorizer.fit_transform(x_train)
X_test = vectorizer.transform(x_test)

param_grid = {
    'C': [0.01, 0.1, 1, 10, 100, 1000],

grid = GridSearchCV(

grid.fit(X_train, y_train)
pred = grid.predict(X_test)

print(grid.best_score_)                      # returns 0.829
print(metrics.precision_score(y_test, pred)) # returns 0.768

now from my understanding, shouldn't the last 2 values be the same? shouldn't grid.best_score_ return the best precision found and that should be equal to the precision_score calculated by the metrics module? The values actually differ quite a bit and i am still trying to figure out why.


grid.best_score_ is the result of cross-validation on train dataset while metrics.precision_score(y_test, pred) is calculated on the test dataset prediction.

| cite | improve this answer | |
  • $\begingroup$ That makes an awful lot of sense. I got confused by the cross-validation which internally uses train- and testing sets vs. my own train- and testing sets. Thanks! $\endgroup$ – bmurauer Apr 27 '16 at 9:41

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.