Predict Class Labels using a Random Forest Classifier Object

Predicts class labels and probabilities for new data using a fitted rf_classifier object. This method projects the new_data into the component space and then uses the stored randomForest model to predict outcomes.

Usage

# S3 method for class 'rf_classifier'
predict(object, new_data, ncomp = NULL, colind = NULL, ...)

Arguments

object: A fitted object of class rf_classifier.
new_data: A numeric matrix or vector of new observations to classify. Rows are observations, columns are variables matching the original data space used by the projector OR matching colind if provided.
ncomp: Optional integer; the number of components to use from the projector for classification (default: all components used during classifier creation).
colind: Optional numeric vector specifying column indices from the original data space. If provided, new_data is projected using only these features (partial_project). This overrides any colind stored default in the object. The resulting projection is compared against the reference scores (object$scores) stored in the classifier.
...: Extra arguments passed to predict.randomForest.

Value

A list containing:

class: Predicted class labels (typically factor) from the random forest model.
prob: A numeric matrix of predicted class probabilities from the random forest model.

Usage

Arguments

Value

See also