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Exercise

Mean target encoding

First of all, you will create a function that implements mean target encoding. Remember that you need to develop the two following steps:

  1. Calculate the mean on the train, apply to the test
  2. Split train into K folds. Calculate the out-of-fold mean for each fold, apply to this particular fold

Each of these steps will be implemented in a separate function: test_mean_target_encoding() and train_mean_target_encoding(), respectively.

The final function mean_target_encoding() takes as arguments: the train and test DataFrames, the name of the categorical column to be encoded, the name of the target column and a smoothing parameter alpha. It returns two values: a new feature for train and test DataFrames, respectively.

Instructions 1/3
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  • You need to add smoothing to avoid overfitting. So, add \(\alpha\) parameter to the denominator in train_statistics calculations.
  • You need to treat new categories in the test data. So, pass a global mean as an argument to the fillna() method.
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