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  3. Machine Learning with Tree-Based Models in Python
  • 1

    Classification and Regression Trees

    Gratuit

    Classification and Regression Trees (CART) are a set of supervised learning models used for problems involving classification and regression. In this chapter, you'll be introduced to the CART algorithm.

  • 2

    The Bias-Variance Tradeoff

    The bias-variance tradeoff is one of the fundamental concepts in supervised machine learning. In this chapter, you'll understand how to diagnose the problems of overfitting and underfitting. You'll also be introduced to the concept of ensembling where the predictions of several models are aggregated to produce predictions that are more robust.

  • 3

    Bagging and Random Forests

    Bagging is an ensemble method involving training the same algorithm many times using different subsets sampled from the training data. In this chapter, you'll understand how bagging can be used to create a tree ensemble. You'll also learn how the random forests algorithm can lead to further ensemble diversity through randomization at the level of each split in the trees forming the ensemble.

  • 4

    Boosting

    Boosting refers to an ensemble method in which several models are trained sequentially with each model learning from the errors of its predecessors. In this chapter, you'll be introduced to the two boosting methods of AdaBoost and Gradient Boosting.

  • 5

    Model Tuning

    The hyperparameters of a machine learning model are parameters that are not learned from data. They should be set prior to fitting the model to the training set. In this chapter, you'll learn how to tune the hyperparameters of a tree-based model using grid search cross validation.

Initializing

Exercice

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.

Instructions

100 XP

  • 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