lasso_vars: Most Relevant Features Using Lasso Regression

In laresbernardo/lares: Analytics & Machine Learning Sidekick

Most Relevant Features Using Lasso Regression

Description

Use Lasso regression to identify the most relevant variables that can predict/identify another variable. You might want to compare with corr_var() and/or x2y() results to compliment the analysis No need to standardize, center or scale your data. Tidyverse friendly.

Usage

lasso_vars(
  df,
  variable,
  ignore = NULL,
  nlambdas = 100,
  nfolds = 10,
  top = 20,
  quiet = FALSE,
  seed = 123,
  ...
)

Arguments

df	Dataframe. Any dataframe is valid as ohse will be applied to process categorical values, and values will be standardize automatically.
variable	Variable. Dependent variable or response.
ignore	Character vector. Variables to exclude from study.
nlambdas	Integer. Number of lambdas to be used in a search.
nfolds	Integer. Number of folds for K-fold cross-validation (>= 2).
top	Integer. Plot top n results only.
quiet	Boolean. Keep quiet? Else, show messages.
seed	Numeric.
...	Additional parameters passed to ohse().

Value

List. Contains lasso model coefficients, performance metrics, the actual model fitted and a plot.

See Also

Other Machine Learning: ROC(), conf_mat(), export_results(), gain_lift(), h2o_automl(), h2o_predict_MOJO(), h2o_selectmodel(), impute(), iter_seeds(), model_metrics(), model_preprocess(), msplit()

Other Exploratory: corr_cross(), corr_var(), crosstab(), df_str(), distr(), freqs(), freqs_df(), freqs_list(), freqs_plot(), missingness(), plot_cats(), plot_df(), plot_nums(), tree_var()

Examples

## Not run: 
# CRAN
Sys.unsetenv("LARES_FONT") # Temporal
data(dft) # Titanic dataset

m <- lasso_vars(dft, Survived, ignore = c("Cabin"))
print(m$coef)
print(m$metrics)
plot(m$plot)

## End(Not run)

laresbernardo/lares documentation built on Oct. 23, 2024, 12:05 p.m.