TidyX 55: Decision Trees, Random Forest, & Optimization

Ellis Hughes and I continue our series on classification of MLB pitch types by working with Decision Trees and Random Forests.

We discuss:

  • Building a decision tree
  • Building a random forest
  • The advantage of random forests over decision trees
  • Tuning the random forest using the {caret} package using parallel processing
  • Evaluating the model’s classification accuracy overall and within pitch

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.

Previous screen casts in this series:

  1. Episode 53: Pitch Classification & EDA
  2. Episode 54: KNN & UMAP