Using cross_val_predict against test data set

Question

I'm confused about using cross_val_predict in a test data set.

I created a simple Random Forest model and used cross_val_predict to make predictions:

from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import cross_val_predict, KFold

lr = RandomForestClassifier(random_state=1, class_weight="balanced", n_estimators=25, max_depth=6)
kf = KFold(train_df.shape[0], random_state=1)
predictions = cross_val_predict(lr,train_df[features_columns], train_df["target"], cv=kf)
predictions = pd.Series(predictions)

I'm confused on the next step here. How do I use what is learnt above to make predictions on the test data set?

Answer 1

I don't think cross_val_score or cross_val_predict uses fit before predicting. It does it on the fly. If you look at the documentation (section 3.1.1.1), you'll see that they never mention fit anywhere.

Answer 2

As @DmitryPolonskiy commented, the model has to be trained (with the fit method) before it can be used to predict.

# Train the model (a.k.a. `fit` training data to it).
lr.fit(train_df[features_columns], train_df["target"])
# Use the model to make predictions based on testing data.
y_pred = lr.predict(test_df[feature_columns])
# Compare the predicted y values to actual y values.
accuracy = (y_pred == test_df["target"]).mean()

cross_val_predict is a method of cross validation, which lets you determine the accuracy of your model. Take a look at sklearn's cross-validation page.