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 a 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) quantization, ii) visualization, iii) feature engineering, iv) feature selection and,
v) model validation. For more details, please read Belbahri et Al. (2019)
<https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>.

LassoPath: LASSO path for penalized logistic regression

Description

Usage

Arguments

Value

References

See Also

Examples