PSL_Book
  • 1. Introduction
  • 2. Linear Regression
  • 3. Variable Selection and Regularization
  • 4. Regression Trees and Ensemble
  • 5. Nonlinear Regression
  • 6. Clustering Analysis
  • 7. Latent Structure Models
  • 8. TBA
  • 9. Discriminant Analysis
  • 10. Logistic Regression
    • 10.1. Setup
    • 10.2. MLE
    • 10.3. Seperable Data
    • 10.4. R/Python Code
    • 10.5. Retrospective Sampling Data
  • 11. Support Vector Machine
  • 12. Classification Trees and Boosting
  • 13. Recommender System
PSL_Book
  • 10. Logistic Regression

10. Logistic Regression

  • 10.1. Setup
  • 10.2. MLE
  • 10.3. Seperable Data
  • 10.4. R/Python Code
  • 10.5. Retrospective Sampling Data
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