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Wrap-up

1. Wrap-up

Congratulations on all that you have accomplished. By taking this course, you mastered many foundations of machine learning in general and tree-based models in particular.

2. 1. Data splitting - confusion matrix - accuracy

In Chapter 1, you learned about basic techniques of machine learning like data splitting and how to use tidymodels to build and evaluate basic classification trees using the confusion matrix and the accuracy metric.

3. 2. Regression - cross-validation - bias-variance tradeoff

In Chapter 2, you extended this knowledge to build and evaluate regression trees, apply cross-validation for more robust evaluation, and how to detect and solve bias or variance issues.

4. 3. Tuning - AUC - bagging - random forest

In Chapter 3, you learned how to use the tidymodels framework to tune hyperparameters of tree models, and extended your knowledge of classification evaluation to sensitivity, specificity, ROC curves and area under the curve. You also learned how to build bagged tree models and random forests.

5. 4. Boosting & model comparison

In Chapter 4, you learned about boosting, how to build an optimal gradient boosting ensemble model, and how to compare different models to see which one performs best.

6. Thank you!

I hope that you've enjoyed this course and found it helpful. Thanks again for taking the course and congratulations on all that you have accomplished. I wish you the best of luck on your journey of learning.