LassoPath

a data frame containing the treatment, the outcome and the predictors.

data

an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.

formula

the number of lambda values - Default is 100.

nb.lambda

Fit an interaction uplift model via penalized maximum likelihood. The regularization path is computed for the lasso penalty at a grid of values for the regularization parameter lambda.

lasso

Uplift modeling aims at predicting the causal effect of an action such as medical treatment or a marketing campaign on a particular individual by taking into consideration the response to a treatment. In order to simplify the task for practitioners in uplift modeling, we propose a combination of tools that can be separated into the following ingredients: i) categorization, ii) visualization, iii) feature engineering, iv) feature selection and v) model validation. For a review of uplift modeling, please read Gutierrez and G<c3><a9>rardy (2017) <http://proceedings.mlr.press/v67/gutierrez17a.html>.

LassoPath: LASSO path for penalized logistic regression

Description

Usage

Arguments

Value

References

See Also

Examples