Random Forest

Steps

1. Create S different dataset from the raw dataset, len(s) = B
   1. They will be slightly different as they are taken random with replacement
   2. On each dataset take random subset of features rather than all features
2. Learn S different decision trees
3. Combine them for prediction
   1. For regression, take the average
   2. For classification, take the majority vote

[!question] What is the advantages of Random Forest?

• As random sample is done, we reduce the effect of noise, outliers, and imbalanced dataset

• So Random forest is good at Handling Missing Data, Handling Outliers, Handling Imbalanced Dataset
• But bad for Interpretability which is far better in Decision Tree