Grouping data points based on similarity

Using labeled data to predict categories

Predicting continuous numeric values

Learning patterns without labeled outcomes

When predicting continuous numeric values

When the dataset is balanced across classes

When monitoring model performance over time

When prediction errors are measured as percentages

One deep tree is better than many small trees

Each tree must see the entire dataset

Combining multiple trees improves predictions

Trees must use all features at every split