Portfolio Builder Exercise #3

Using the same dataset leveraged for Portfolio Builder Exercise #1 & #2, write up a third report that answers the following:

1. Apply a MARS model with all features.
   • How does the model performance compare to your previous models?
   • How many of the features are influential? Which 10 features are considered most influential?
   • Does your model include hinge functions? If so, explain their coefficient and plot their impact on the predicted response variable.
   • Does your model include interactions? If so, pick the interaction effect that is most influential and explain the coefficient.
2. Apply a random forest model.
   • First, apply a default random forest model.
   • Now apply a a full cartesian grid search across various values of \(m_{try}\), tree complexity & sampling scheme.
   • Now run a random grid search across the same hyperparameter grid but restrict the time or number of models to run to 50% of the models ran in the full cartesian.
3. Pick the best performing model from above.
   • Identify the most influential features for this model.
   • Plot the top 10 most influential features.
   • Do these features have positive or negative impacts on your response variable?
   • Create partial dependence plots for these features. Explain the relationship between the feature and the predicted values.

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