Case studies
glmnet is (almost) all you need
Preface
Dedication
Introduction
Basics
1
What is supervised learning?
2
Regularized regression
3
Details on the lambda sequence
4
Cross-validation
5
Standardization
6
Preparing the input matrix
Generalized Linear Models (GLMs)
7
Binary classification
8
Multiclass classification
9
Poisson regression
10
Other GLMs
11
Multi-task learning
Survival models
12
Cox regression
13
Stratified Cox models
14
Cox regression for start-stop data
Assessing models
15
Assessing models
Specialized features
16
Upper and lower limits
17
Offsets
18
Filtering
19
Penalty factors
20
The relaxed lasso
21
Miscellaneous features
Friends of glmnet
22
glmnet in python: the adelie package
23
Tidy modeling with glmnet
24
glmnet’s limitations
25
The adelie package
26
Other regularized regression models
Case studies
27
Predicting molecular breast cancer subtypes from the METABRIC data
References
Appendices
A
Comparing glmnet and CVX
B
How we compute
\(\lambda_{max}\)
C
Comparing CV AUC with
pROC::roc
D
Comparing glmnet and scikit-learn
Case studies
TODO: Add chapter descriptions.
26
Other regularized regression models
27
Predicting molecular breast cancer subtypes from the METABRIC data