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: