Tuning preparation

Tuning preparation is the foundation for tuning success. There are two main steps in preparing your tuning: marking hyperparameters using tune() in the model specification and creating a grid of hyperparameters that is used in tuning.

You are going to execute these two fundamental steps of the tuning process in this exercise.

Este exercício faz parte do curso

Machine Learning with Tree-Based Models in R

Instruções do exercício

Create a boosting specification with an "xgboost" engine for a classification model using 500 trees and mark the following parameters as tuning parameters: learn_rate, tree_depth, and sample_size. Save the result as boost_spec.
Build a regular tuning grid for the tuning parameters of boost_spec with three levels for each parameter.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Create the specification with placeholders
boost_spec <- boost_tree(
                trees = ___,
                ___,
                ___,
                ___) %>%
  set_mode(___) %>%
  set_engine(___)

# Create the tuning grid
tunegrid_boost <- ___(___, 
                      levels = ___)

tunegrid_boost

Editar e executar o código

Este exercício faz parte do curso

Machine Learning with Tree-Based Models in R

InicianteNível de habilidade

4.9+

Iniciar curso de graça

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.

Exercise 1: Welcome to the course!Exercise 2: Why tree-based methods?Exercise 3: Specify that tree Exercise 4: Train that model Exercise 5: How to grow your tree Exercise 6: Train/test split Exercise 7: Avoiding class imbalances Exercise 8: From zero to hero Exercise 9: Predict and evaluate Exercise 10: Make predictions Exercise 11: Crack the matrix Exercise 12: Are you predicting correctly?

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.

Exercise 1: Continuous outcomes Exercise 2: Train a regression tree Exercise 3: Predict new values Exercise 4: Inspect model output Exercise 5: Performance metrics for regression trees Exercise 6: In-sample performance Exercise 7: Out-of-sample performance Exercise 8: Bigger mistakes, bigger penalty Exercise 9: Cross-validation Exercise 10: Create the folds Exercise 11: Fit the folds Exercise 12: Evaluate the folds Exercise 13: Bias-variance tradeoff Exercise 14: Call things by their names Exercise 15: Adjust model complexity Exercise 16: In-sample and out-of-sample performance

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.

Exercise 1: Tuning hyperparameters Exercise 2: Generate a tuning grid Exercise 3: Tune along the grid Exercise 4: Pick the winner Exercise 5: More model measures Exercise 6: Calculate specificity Exercise 7: Draw the ROC curve Exercise 8: Area under the ROC curve Exercise 9: Bagged trees Exercise 10: Create bagged trees Exercise 11: In-sample ROC and AUC Exercise 12: Check for overfitting Exercise 13: Random forest Exercise 14: Bagged trees vs. random forest Exercise 15: Variable importance

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.

Exercise 1: Introduction to boosting Exercise 2: Bagging vs. boosting Exercise 3: Specify a boosted ensemble Exercise 4: Gradient boosting Exercise 5: Train a boosted ensemble Exercise 6: Evaluate the ensemble Exercise 7: Compare to a single classifier Exercise 8: Optimize the boosted ensemble Exercise 9: Tuning preparation

Exercício atual

Exercise 10: The actual tuning Exercise 11: Finalize the model Exercise 12: Model comparison Exercise 13: Compare AUC Exercise 14: Plot ROC curves Exercise 15: Wrap-up