Skip to main content

Ctrl+K

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

.md

Measuring Performance in Regression Models

Contents

5.1.1. Quantitative Measures of Performance
5.1.2. The Variance-Bias Trade-off
5.1.3. Computing

5.1. Measuring Performance in Regression Models#

5.1.1. Quantitative Measures of Performance#

5.1.2. The Variance-Bias Trade-off#

5.1.3. Computing#

previous

5. Chapter 5

next

6. Chapter 6

Contents

5.1.1. Quantitative Measures of Performance
5.1.2. The Variance-Bias Trade-off
5.1.3. Computing

By Gustavo Argote

© Copyright 2022.