Set the hyperparameter grid of RF

In this exercise, you'll manually set the grid of hyperparameters that will be used to tune rf's hyperparameters and find the optimal regressor. For this purpose, you will be constructing a grid of hyperparameters and tune the number of estimators, the maximum number of features used when splitting each node and the minimum number of samples (or fraction) per leaf.

This exercise is part of the course

Machine Learning with Tree-Based Models in Python

Exercise instructions

Define a grid of hyperparameters corresponding to a Python dictionary called params_rf with:
- the key 'n_estimators' set to a list of values 100, 350, 500
- the key 'max_features' set to a list of values 'log2', 'auto', 'sqrt'
- the key 'min_samples_leaf' set to a list of values 2, 10, 30

Hands-on interactive exercise

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

# Define the dictionary 'params_rf'
params_rf = ____

Edit and Run Code

This exercise is part of the course

Machine Learning with Tree-Based Models in Python

IntermediateSkill Level

4.9+

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Exercise 1: Adaboost Exercise 2: Define the AdaBoost classifier Exercise 3: Train the AdaBoost classifier Exercise 4: Evaluate the AdaBoost classifier Exercise 5: Gradient Boosting (GB)Exercise 6: Define the GB regressor Exercise 7: Train the GB regressor Exercise 8: Evaluate the GB regressor Exercise 9: Stochastic Gradient Boosting (SGB)Exercise 10: Regression with SGB Exercise 11: Train the SGB regressor Exercise 12: Evaluate the SGB regressor

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Exercise 1: Tuning a CART's Hyperparameters Exercise 2: Tree hyperparameters Exercise 3: Set the tree's hyperparameter grid Exercise 4: Search for the optimal tree Exercise 5: Evaluate the optimal tree Exercise 6: Tuning a RF's Hyperparameters Exercise 7: Random forests hyperparameters Exercise 8: Set the hyperparameter grid of RF

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Exercise 9: Search for the optimal forest Exercise 10: Evaluate the optimal forest Exercise 11: Congratulations!