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

    Classification Trees

    Gratuito

    Ready to build a real machine learning pipeline? Complete step-by-step exercises to learn how to create decision trees, split your data, and predict which patients are most likely to suffer from diabetes. Last but not least, you’ll build performance measures to assess your models and judge your predictions.

  • 2

    Regression Trees and Cross-Validation

    Ready for some candy? Use a chocolate rating dataset to build regression trees and assess their performance using suitable error measures. You’ll overcome statistical insecurities of single train/test splits by applying sweet techniques like cross-validation and then dive even deeper by mastering the bias-variance tradeoff.

  • 3

    Hyperparameters and Ensemble Models

    Time to get serious with tuning your hyperparameters and interpreting receiver operating characteristic (ROC) curves. In this chapter, you’ll leverage the wisdom of the crowd with ensemble models like bagging or random forests and build ensembles that forecast which credit card customers are most likely to churn.

  • 4

    Boosted Trees

    Ready for the high society of tree-based models? Apply gradient boosting to create powerful ensembles that perform better than anything that you have seen or built. Learn about their fine-tuning and how to compare different models to pick a winner for production.

Initializing

Exercicio

Generate a tuning grid

The standard hyperparameters of most models provide a good fit for most datasets. Yet, they need optimization for the best performance. Otherwise, it's like driving a car with an activated hand brake. Release the brake and tune your models!

In this exercise, you'll create two objects that serve as a starting point: a tuning grid (a set of hyperparameter combinations) and a model specification that you later train with every value of the grid.

Instruções 1/2

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  • Create an rpart powered classification tree specification, flagging the tree_depth and cost_complexity parameters for tuning.