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Instantiate the model

In the following set of exercises, you'll diagnose the bias and variance problems of a regression tree. The regression tree you'll define in this exercise will be used to predict the mpg consumption of cars from the auto dataset using all available features.

We have already processed the data and loaded the features matrix X and the array y in your workspace. In addition, the DecisionTreeRegressor class was imported from sklearn.tree.

This is a part of the course

“Machine Learning with Tree-Based Models in Python”

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Exercise instructions

  • Import train_test_split from sklearn.model_selection.
  • Split the data into 70% train and 30% test.
  • Instantiate a DecisionTreeRegressor with max depth 4 and min_samples_leaf set to 0.26.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import train_test_split from sklearn.model_selection
____

# Set SEED for reproducibility
SEED = 1

# Split the data into 70% train and 30% test
X_train, X_test, y_train, y_test = ____(____, ____, test_size=____, random_state=SEED)

# Instantiate a DecisionTreeRegressor dt
dt = ____(____=____, ____=____, random_state=SEED)
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