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Exercise

KFold cross validation

When working with ML models, it's essential to evaluate their performance on unseen data while ensuring that. One common technique for this purpose is k-fold cross-validation. In this exercise, you'll explore how the k-fold cross-validation technique splits a dataset into training and testing sets. KFold is imported for you, as well as the heart disease dataset features heart_disease_df_X.

Instructions

100 XP
  • Create a KFold object with k=5, shuffle=True, and random_state=42
  • Split the data using kfold.split()
  • Print out the number of datapoints in the train and test splits