K-fold cross-validation

You will work with a binary classification problem on a subsample from Kaggle playground competition. The objective of this competition is to predict whether a famous basketball player Kobe Bryant scored a basket or missed a particular shot.

Train data is available in your workspace as bryant_shots DataFrame. It contains data on 10,000 shots with its properties and a target variable "shot\_made\_flag" -- whether shot was scored or not.

One of the features in the data is "game_id" -- a particular game where the shot was made. There are 541 distinct games. So, you deal with a high-cardinality categorical feature. Let's encode it using a target mean!

Suppose you're using 5-fold cross-validation and want to evaluate a mean target encoded feature on the local validation.

This exercise is part of the course

Winning a Kaggle Competition in Python

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Exercise instructions

To achieve this, you need to repeat encoding procedure for the "game_id" categorical feature inside each folds split separately. Your goal is to specify all the missing parameters for the mean_target_encoding() function call inside each folds split.
Recall that the train and test parameters expect the train and test DataFrames.
While the target and categorical parameters expect names of the target variable and categorical feature to be encoded.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create 5-fold cross-validation
kf = KFold(n_splits=5, random_state=123, shuffle=True)

# For each folds split
for train_index, test_index in kf.split(bryant_shots):
    cv_train, cv_test = bryant_shots.iloc[train_index], bryant_shots.iloc[test_index]

    # Create mean target encoded feature
    cv_train['game_id_enc'], cv_test['game_id_enc'] = mean_target_encoding(train=cv_train,
                                                                           test=____,
                                                                           target='shot_made_flag',
                                                                           categorical='____',
                                                                           alpha=5)
    # Look at the encoding
    print(cv_train[['game_id', 'shot_made_flag', 'game_id_enc']].sample(n=1))

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