Predict Class Labels using a Classifier Object

Predicts class labels and probabilities for new data using a fitted classifier object. It performs k-Nearest Neighbors (k-NN) classification in the projected component space.

Usage

# S3 method for class 'classifier'
predict(
  object,
  new_data,
  ncomp = NULL,
  colind = NULL,
  metric = c("euclidean", "cosine", "ejaccard"),
  normalize_probs = FALSE,
  prob_type = c("knn_proportion", "avg_similarity"),
  ...
)

Arguments

object

A fitted object of class 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.

metric

Character string specifying the similarity or distance metric for k-NN. Choices: "euclidean", "cosine", "ejaccard".

normalize_probs

Logical; DEPRECATED Normalization behavior is now implicit in prob_type="avg_similarity".

prob_type

Character string; method for calculating probabilities:

• "knn_proportion" (default): Calculates the proportion of each class among the k nearest neighbors.

• "avg_similarity": Calculates average similarity to all training points per class (uses avg_probs helper).

...

Extra arguments passed down to projection methods (project, partial_project) or potentially to distance/similarity calculations (e.g., for proxy::simil if used with ejaccard).

Value

A list containing:

class

A factor vector of predicted class labels for new_data.

prob

A numeric matrix (rows corresponding to new_data, columns to classes) of estimated class probabilities.

Details

The function first projects the new_data into the component space defined by the classifier's internal projector. If colind is specified, a partial projection using only those features is performed. This projection is then compared to the reference scores stored within the classifier object (object$scores) using the specified metric. The k-NN algorithm identifies the k nearest reference samples (based on similarity or distance) and predicts the class via majority vote. Probabilities are estimated based on the average similarity/distance to each class among the neighbors or all reference points.

See also

classifier.projector, classifier.multiblock_biprojector, partial_project

Other classifier predict: predict.rf_classifier()

Examples

# Assume clf is a fitted classifier object (e.g., from classifier.projector)
# Assume new_dat is a matrix of new observations
# preds <- predict(clf, new_data = new_dat, metric = "cosine")
# print(preds$class)
# print(preds$prob)