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

    Classification and Regression Trees

    Free

    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

Exercise

Evaluate the training error

You'll now evaluate the training set RMSE achieved by the regression tree dt that you instantiated in a previous exercise.

In addition to dt, X_train and y_train are available in your workspace.

Note that in scikit-learn, the MSE of a model can be computed as follows:

MSE_model = mean_squared_error(y_true, y_predicted)

where we use the function mean_squared_error from the metrics module and pass it the true labels y_true as a first argument, and the predicted labels from the model y_predicted as a second argument.

Instructions

100 XP
  • Import mean_squared_error as MSE from sklearn.metrics.
  • Fit dt to the training set.
  • Predict dt's training set labels and assign the result to y_pred_train.
  • Evaluate dt's training set RMSE and assign it to RMSE_train.