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  • Welcome to Applied Predictive Modeling

APM Book

  • 1. Chapter 1
    • 1.1. Introduction
  • 2. Chapter 2
    • 2.1. A Short Tour of the Predictive Modeling Process
  • 3. Chapter 3
    • 3.1. Data Pre-processing
  • 4. Chapter 4
    • 4.1. Over-Fitting and Model Tuning
  • 5. Chapter 5
    • 5.1. Measuring Performance in Regression Models
  • 6. Chapter 6
    • 6.1. Linear Regression and Its Cousins
  • 7. Chapter 7
    • 7.1. Nonlinear Regression Models
  • 8. Chapter 8
    • 8.1. Regression Trees and Rule-Based Models
  • 9. Chapter 9
    • 9.1. A Summary of Solubility Models
  • 10. Chapter 10
    • 10.1. Case Study: Compressive Strength of Concrete Mixtures
  • 11. Chapter 11
    • 11.1. Measuring Performance in Classification Models
  • 12. Chapter 12
    • 12.1. Discriminant Analysis and Other Linear Classification Models
  • 13. Chapter 13
    • 13.1. Nonlinear Classification Models
  • 14. Chapter 14
    • 14.1. Classification Trees and Rule-Based Models
  • 15. Chapter 15
    • 15.1. A Summary of Grant Application Models
  • 16. Chapter 16
    • 16.1. Remedies for Severe Class Imbalance
  • 17. Chapter 17
    • 17.1. Case Study: Job Scheduling
  • 18. Chapter 18
    • 18.1. Measuring Predictor Importance
  • 19. Chapter 19
    • 19.1. An Introduction to Feature Selection
  • 20. Chapter 20
    • 20.1. Factors That Can Affect Model Performance
  • 21. Appendix
    • 21.1. Appendix

APM Module

  • applied_predictive_modeling Documentation
    • applied_predictive_modeling
      • applied_predictive_modeling.test

APM Project

  • Frequently Asked Questions (FAQ)
  • Repository
  • Open issue

Index

A | F | M

A

  • applied_predictive_modeling
    • module
  • applied_predictive_modeling.test
    • module

F

  • f() (in module applied_predictive_modeling.test)

M

  • module
    • applied_predictive_modeling
    • applied_predictive_modeling.test

By Gustavo Argote

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