9
Classification Models
R Note for Statistics Learning and Computing
Preface
1
R Basics
2
R Programming for Data Analysis
3
Datasets to Practice
4
Hypothesis Test
5
Linear Regression
6
ANalysis of VAriance (ANOVA)
7
Logistics Regression
8
Generalized Linear Regression
9
Classification Models
10
Neural Network
11
Decision Tree and Random Forests
12
Generate Random Variables
13
Monte Carlo
References
Table of contents
9.1
Performance
9
Classification Models
9.1
Performance
Please see Logistics -> Performance
8
Generalized Linear Regression
10
Neural Network