scoring in grid search is test scoring or test scoring?
<p>I am using grid search to find right parameters. </p>

<p>Following is my code.</p>

<pre><code>from sklearn.ensemble import GradientBoostingClassifier
gbm = GradientBoostingClassifier(random_state=2)

parameters = {"loss" : ("deviance", "exponential"), \
              "learning_rate":[0.1, 0.01, 0.02, 0.05], \
              "n_estimators":[100, 500, 1000, 5000],\
             "max_depth":[1,3,5],\
             "subsample":[0.5, 0.8, 0.9, 1],\
             "max_features":("sqrt", "log2", 1.0, 0.2, 0.5, 0.8)}

from sklearn import grid_search
gs = grid_search.GridSearchCV(gbm, parameters, scoring="log_loss", n_jobs=-1,cv=3, verbose=5)
gs.fit(train, y)
</code></pre>

<p>my question here is, how does scoring(here log_loss) is calcuated ? it is log_loss of data which, are used in building model or data, which are left out during each CV and then average of scoring ?</p>