Prediction Method for mml Objects

Source: R/generic-s3.R

Prediction method for class mml.

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

Arguments

object

An mml 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.

Value

Matrix of predictions.

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.

References

  • Di Francesco, R. (2023). Ordered Correlation Forest. arXiv preprint arXiv:2309.08755.

Author

Riccardo Di Francesco

Examples

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

data <- generate_ordered_data(1000)
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))
#>         P(Y=1)    P(Y=2)    P(Y=3)    P(Y=1)    P(Y=2)     P(Y=3)
#> [1,] 0.2750897 0.5810172 0.1438932 0.4743307 0.3381195 0.18754984
#> [2,] 0.3326762 0.4868678 0.1804560 0.4722287 0.3206559 0.20711540
#> [3,] 0.2855366 0.3379332 0.3765303 0.2912424 0.3013192 0.40743847
#> [4,] 0.4088999 0.4011857 0.1899144 0.4187626 0.3195804 0.26165697
#> [5,] 0.6617388 0.2228519 0.1154092 0.6312130 0.2924961 0.07629089
#> [6,] 0.4125682 0.3427655 0.2446663 0.4852413 0.3126977 0.20206096