## 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))
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