The goal of ML is to provide a simple to use package to estimate Lasso, Ridge, Random Forest (RF), Conditional Inference Forest (CIF), Extreme Gradient Boosting (XGB), Catboosting (CB), Logit lasso and a SuperLearner combining all of these learners. The package has three user functions: modest, FVest and MLest. modest estimates only the model of the chosen Machine Learner. FVest takes a model and gives the predicted fitted values for new features of choice. MLest combines both functions but only computes fitted values of the same observations used to build the model. The package includes a survey with income and circumstances for Madrid in 2018 (from Encuesta de Condiciones de vida (ECV) 2019).

Installation

You can install the development version of ML from GitHub with:

# install devtools if not installed
install.packages("devtools")
# install ML from github
devtools::install_github("joelters/ML")

Examples of the three functions are

X <- dplyr::select(mad2019,-Y)
Y <- mad2019$Y

m1 <- modest(X,Y,"RF")

m2 <- modest(X,Y,"SL",
        ensemble = c("SL.Lasso","SL.Ridge","SL.RF","SL.CIF","SL.XGB","SL.CB"))
      
FVs1 <- FVest(m1,X,Y,X[1:5,],Y[1:5],ML = "RF")

FVs2 <- FVest(m2,X,Y,ML = "SL")

m3 <- MLest(X,Y,"Lasso", FVs = FALSE)

m4 <- MLest(X,Y,"SL",
        ensemble = c("SL.Lasso","SL.Ridge","SL.RF","SL.CIF","SL.XGB","SL.CB"))

For more info install the package and see the documentation of the functions with ?modest, ?FVest and ?MLest. For now it is not possible to change the tuning parameters. For the moment I suggest using the trace() function to change the tuning parameters (see here).