Final thoughts

Pros:

• Naturally handles mixed types of predictors (quantitative and qualitative)
• Requires minimal feature engineering
• Performs automated feature selection
• Highly **correlated predictors* do not impede predictive accuracy
• Finds the important nonlinear interactions present in the data

Cons:

• Slower to train
• Correlated predictors can make model interpretation difficult