Random Forest Vignette

Click Here for a video going through how to code up decision tree and random forest models

I have uploaded three coding videos where I go through an example of using decision trees, pruning, bagging, and random forests to predict the log salary of the MLB hitters dataset. This example partially follows the coding lab at the end of ISLP, and also follows the examples that I showed in the meetup.

Click below to watch the videos:

Video Link	Runtime
Random Forest Vignette 3: Out-of-Bag Error, Feature Importance, and Partial Dependence	20:30
Random Forest Vignette 2: Bagging and ‘max_features’	14:54
Random Forest Vignette 1: Decision Trees and Cost-Complexity Pruning	18:53

The code for the vignette can be found in

• trees_bagging_rf_vignette_.ipynb or
• trees_bagging_vignette_live.ipynb