2. Understanding why a classifier predicts

• 2.1. Interpreting linear models
  • 2.1.1. Data on wages
  • 2.1.2. The challenge of correlated features
  • 2.1.3. Coefficients of a linear model
  • 2.1.4. The effect of regularization
  • 2.1.5. Stability to gauge significance
    • 2.1.5.1. With the lasso estimator
    • 2.1.5.2. With the ridge estimator
    • 2.1.5.3. Which is the truth?
      • 2.1.5.3.1. Conclusion on factors of wages?
• 2.2. Interpreting random forests
  • 2.2.1. Data on wages
  • 2.2.2. Feature importance
  • 2.2.3. Meaning and Caveats
• 2.3. Partial dependency plots
  • 2.3.1. All-in-one plotting function
  • 2.3.2. Lower-level partial_dependence function
• 2.4. Black-box interpretation of models: LIME
  • 2.4.1. Regression on tabular data: factors of prices of houses

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Interpreting linear models

Interpreting random forests

Partial dependency plots

Black-box interpretation of models: LIME