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:
- Split train data into two parts
- Train multiple models on Part 1
- Make predictions on Part 2
- Make predictions on the test data
- Train a new model on Part 2 using predictions as features
- 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".
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Winning a Kaggle Competition in Python
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from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingRegressor, RandomForestRegressor
# Split train data into two parts
part_1, part_2 = ____(train, test_size=____, random_state=123)
# Train a Gradient Boosting model on Part 1
gb = GradientBoostingRegressor().____(____[features], ____.fare_amount)
# Train a Random Forest model on Part 1
rf = RandomForestRegressor().____(____[features], ____.fare_amount)