Train that model

A model specification is a good start, just like the canvas for a painter. But just as a painter needs color, the specification needs data. Only the final model is able to make predictions:

Model specification + data = model

In this exercise, you will train a decision tree that models the risk of diabetes using health variables as predictors. The response variable, outcome, indicates whether the patient has diabetes or not, which means this is a binary classification problem (there are just two classes). The dataset also contains health variables of patients like blood_pressure, age, and bmi.

For the rest of the course, the tidymodels package will always be pre-loaded. In this exercise, the diabetes dataset is also available in your workspace.

Diese Übung ist Teil des Kurses

Machine Learning with Tree-Based Models in R

Anleitung zur Übung

Create tree_spec, a specification for a decision tree with an rpart engine.
Train a model tree_model_bmi, where the outcome depends only on the bmi predictor by fitting the diabetes dataset to the specification.
Print the model to the console.

Interaktive Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Create the specification
tree_spec <- ___() %>% 
  ___("rpart") %>% 
  ___

# Train the model
tree_model_bmi <- tree_spec %>% 
  ___

# Print the model
___

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

Machine Learning with Tree-Based Models in R

Mittlere SchwierigkeitSchwierigkeitsgrad

4.9+

Kurs kostenlos starten

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

Aktuelle Übung

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 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