interpret: Interpret a model via regularized coefficient estimates

In healthcareai: Tools for Healthcare Machine Learning

Interpret a model via regularized coefficient estimates

Description

Interpret a model via regularized coefficient estimates

Usage

interpret(x, sparsity = NULL, remove_zeros = TRUE, top_n)

Arguments

x	a model_list object containing a glmnet model
sparsity	If NULL (default) coefficients for the best-performing model will be returned. Otherwise, a value in [0, 1] that determines the sparseness of the model for which coefficients will be returned, with 0 being maximally sparse (i.e. having the fewest non-zero coefficients) and 1 being minimally sparse
remove_zeros	Remove features with coefficients equal to 0? Default is TRUE
top_n	Integer: How many coefficients to return? The largest top_n absolute-value coefficients will be returned. If missing (default), all coefficients are returned

Details

**WARNING** Coefficients are on the scale of the predictors; they are not standardized, so unless features were scaled before training (e.g. with prep_data(..., scale = TRUE), the magnitude of coefficients does not necessarily reflect their importance.

If x was trained with more than one value of alpha the best value of alpha is used; sparsity is determined only via the selection of lambda. Using only lasso regression (i.e. alpha = 1) will produce a sparser set of coefficients and can be obtained by not tuning hyperparameters.

Value

A data frame of variables and their regularized regression coefficient estimates with parent class "interpret"

See Also

plot.interpret

Examples

m <- machine_learn(pima_diabetes, patient_id, outcome = diabetes, models = "glm")
interpret(m)
interpret(m, .2)
interpret(m) %>%
  plot()

healthcareai documentation built on Sept. 5, 2022, 5:12 p.m.