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Exercise

Model stacking I

Now it's time for stacking. To implement the stacking approach, you will follow the 6 steps we've discussed in the previous video:

  1. Split train data into two parts
  2. Train multiple models on Part 1
  3. Make predictions on Part 2
  4. Make predictions on the test data
  5. Train a new model on Part 2 using predictions as features
  6. Make predictions on the test data using the 2nd level model

train and test DataFrames are already available in your workspace. features is a list of columns to be used for training on the Part 1 data and it is also available in your workspace. Target variable name is "fare_amount".

Instructions 1/2
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  • Split the train DataFrame into two equal parts: part_1 and part_2. Use the train_test_split() function with test_size equal to 0.5.
  • Train Gradient Boosting and Random Forest models on the part_1 data.