My Data Science Notes
Intro
1
Probability
1.1
Principles
2
Discrete Distributions
2.1
Bernoulli
2.2
Binomial
2.3
Poission
2.4
Multinomial
2.5
Negative-Binomial
2.6
Geometric
2.7
Hypergeometric
2.8
Gamma
3
Continuous Distributions
3.1
Normal
3.1.1
Example
3.1.2
Example
3.1.3
Example
3.1.4
Normal Approximation to Binomial
3.1.5
Example
3.1.6
Example
3.1.7
From Sample to Population
3.2
Join Distributions
3.3
Likelihood
4
Discrete Variables
4.1
Chi-Square Test
4.2
One-Way Tables
4.2.1
Chi-Square Goodness-of-Fit Test
4.2.2
Proportion Test
4.3
Two-Way Tables
4.3.1
Chi-Square Independence Test
4.3.2
Residuals Analysis
4.3.3
Difference in Proportions
4.3.4
Relative Risk
4.3.5
Odds Ratio
4.3.6
Partitioning Chi-Square
4.4
Example of Chi-Square Test of Homogeneity
5
Inference
6
Regression
7
Generalized Linear Models
7.1
Logistic Regression
Example
7.2
Multinomial Logistic Regression
7.3
Ordinal Logistic Regression
7.4
Poisson Regression
Example
8
Classification
9
Classification
10
Decision Trees
10.1
Classification Tree
10.1.1
Confusion Matrix
10.1.2
ROC Curve
10.1.3
Caret Approach
10.2
Regression Trees
10.2.1
Caret Approach
10.3
Bagging
10.4
Random Forests
10.5
Gradient Boosting
10.6
Summary
10.7
Reference
11
Regularization
12
Non-linear Models
12.1
Splines
12.2
MARS
12.3
GAM
13
Support Vector Machines
13.1
Maximal Margin Classifier
13.2
Support Vector Classifier
13.3
Support Vector Machines
13.4
Example
13.5
Using Caret
14
Principal Components Analysis
15
Clustering
16
Text Mining
Appendix
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References
Published with bookdown
My Data Science Notes
Chapter 4
Discrete Variables