ml_make_model: Model factory for tabular cross-sectional learners

In PortfolioTesteR: Test Investment Strategies with English-Like Code

Model factory for tabular cross-sectional learners

Description

Returns a pair of closures fit(X,y) / predict(model, X) implementing a chosen learner. Implementations are NA-aware and conservative: glmnet ridge drops rows with any non-finite input; ranger and xgboost keep NA in X as missing; the linear baseline uses lm.fit.

Usage

ml_make_model(
  type = c("ridge", "rf", "xgboost", "linear"),
  params = list(),
  nrounds = 200L,
  ...
)

Arguments

type	One of "ridge", "rf", "xgboost", "linear".
params	List of model parameters (passed to backend; used by xgboost).
nrounds	Integer boosting rounds (xgboost).
...	Additional arguments forwarded to the backend.

Details

Optional dependencies: glmnet (ridge), ranger (rf), xgboost (xgboost). If a backend is not available, use "linear" or install the package.

Value

A list with functions fit and predict.

Examples

## Not run: 
ridge <- ml_make_model("ridge")
m <- ridge$fit(X_is, y_is)
s <- ridge$predict(m, X_oos)

## End(Not run)

PortfolioTesteR documentation built on Nov. 5, 2025, 5:23 p.m.