predict.oml: Prediction Method for oml Objects

In ocf: Ordered Correlation Forest

Prediction Method for oml Objects

Description

Prediction method for class oml.

Usage

## S3 method for class 'oml'
predict(object, data = NULL, ...)

Arguments

object	An oml object.
data	Data set of class data.frame. It must contain the same covariates used to train the base learners. If data is NULL, then object$X is used.
...	Further arguments passed to or from other methods.

Details

If object$learner == "l1", then model.matrix is used to handle non-numeric covariates. If we also have object$scaling == TRUE, then data is scaled to have zero mean and unit variance.

Value

Matrix of predictions.

Author(s)

Riccardo Di Francesco

References

• Di Francesco, R. (2025). Ordered Correlation Forest. Econometric Reviews, 1–17. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/07474938.2024.2429596")}.

See Also

multinomial_ml, ordered_ml

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_ordered_data(100)
sample <- data$sample
Y <- sample$Y
X <- sample[, -1]

## Training-test split.
train_idx <- sample(seq_len(length(Y)), floor(length(Y) * 0.5))

Y_tr <- Y[train_idx]
X_tr <- X[train_idx, ]

Y_test <- Y[-train_idx]
X_test <- X[-train_idx, ]

## Fit ordered machine learning on training sample using two different learners.
ordered_forest <- ordered_ml(Y_tr, X_tr, learner = "forest")
ordered_l1 <- ordered_ml(Y_tr, X_tr, learner = "l1")

## Predict out of sample.
predictions_forest <- predict(ordered_forest, X_test)
predictions_l1 <- predict(ordered_l1, X_test)

## Compare predictions.
cbind(head(predictions_forest), head(predictions_l1))

ocf documentation built on April 4, 2025, 4:44 a.m.