1.2 Concepts

9.3

Racket

1.2 Concepts🔗ℹ

Every model family in glmnet shares one data layout, one penalty and one shape of result. This chapter covers those shared pieces; the Examples then take each family in turn.

A fit is the result of estimating a model on a dataset. Its solution consists of the parameter values found by minimizing the model’s objective function.

1.2.1 Data layout

1.2.1.1 Named data

1.2.2 The penalty: α and λ

1.2.3 Model families

1.2.4 Results

1.2.5 Regularization paths

1.2.6 Predictions and coefficients

1.2.6.1 What is predicted

1.2.6.2 Predicting along a path

1.2.7 Choosing λ by cross-validation

1.2.7.1 Reading lambda-min and lambda-1se

1.2.7.2 Predicting at the chosen λ

1.2.7.3 Folds

1.2.7.4 Measures of error

1.2.8 Standardization and the intercept

1.2.9 Convergence and errors

1.2.10 The native library

1.1	Getting started
1.2	Concepts
1.3	Data
1.4	Formulas and named data
1.5	Plots
1.6	Examples

1.2.1	Data layout
1.2.2	The penalty: α and λ
1.2.3	Model families
1.2.4	Results
1.2.5	Regularization paths
1.2.6	Predictions and coefficients
1.2.7	Choosing λ by cross-validation
1.2.8	Standardization and the intercept
1.2.9	Convergence and errors
1.2.10	The native library