predict.mml {ocf} | R Documentation |
Prediction Method for mml Objects
Description
Prediction method for class mml
.
Usage
## S3 method for class 'mml'
predict(object, data = NULL, ...)
Arguments
object |
An |
data |
Data set of class |
... |
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. (2023). Ordered Correlation Forest. arXiv preprint arXiv:2309.08755.
See Also
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 multinomial machine learning on training sample using two different learners.
multinomial_forest <- multinomial_ml(Y_tr, X_tr, learner = "forest")
multinomial_l1 <- multinomial_ml(Y_tr, X_tr, learner = "l1")
## Predict out of sample.
predictions_forest <- predict(multinomial_forest, X_test)
predictions_l1 <- predict(multinomial_l1, X_test)
## Compare predictions.
cbind(head(predictions_forest), head(predictions_l1))
[Package ocf version 1.0.1 Index]