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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
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Chapter 18
18.
Chapter 18
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18.1. Measuring Predictor Importance
