• A decision tree is a flowchart-like structure in which each internal node represents a test on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent classification rules. -Wikiedia

Is a:

• method

Applications:

• Capture Interactions In Data
• Classification
• Data Visualization
• Feature Importance
• Recommendation
• Regression

Advantages:

• interpretable
• can deal with data where features interact with each other
• can deal with large, complicated, differently-scaled data
• can deal with non-linear data
• creates good explanations

Disadvantages:

• unstable
• fails to deal with linear relationships without adding splits, which is inefficient
• lack of smoothness; slight changes in an input feature can have a big impact on the predicted outcome
• only easily interpretable when short

Examples:

• 001-DecisionTree