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  3. Winning a Kaggle Competition in Python

Exercise

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.

Instructions

100 XP
  • 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.