.. index:: single: knn .. _knn/0:

.. rst-class:: right

object

knn

k-Nearest Neighbors classifier with multiple distance metrics and weighting options. Learns from a dataset object implementing the dataset_protocol protocol and returns a classifier term that can be used for prediction and exported as predicate clauses.

| Availability: | logtalk_load(knn(loader))

| Author: Paulo Moura | Version: 1:0:0 | Date: 2026-02-20

| Compilation flags: | static, context_switching_calls

| Implements: | public :ref:`classifier_protocol <classifier_protocol/0>` | Imports: | public :ref:`options <options/0>` | Uses: | :ref:`format <format/0>` | :ref:`list <list/0>` | :ref:`pairs <pairs/0>` | :ref:`type <type/0>`

| Remarks:

• Algorithm: k-NN is a lazy learning algorithm that classifies instances based on the majority class among the k nearest training instances.
• Distance metrics: Supports Euclidean, Manhattan, Chebyshev, and Minkowski distance metrics.
• Weighting schemes: Supports uniform, distance-based, and Gaussian weighting of neighbors.
• Feature types: Automatically handles numeric and categorical features.
• Classifier representation: The learned classifier is represented (by default) as a knn_classifier(AttributeNames, FeatureTypes, Instances) where Instances contains the training data.

| Inherited public predicates: |  :ref:`options_protocol/0::check_option/1`  :ref:`options_protocol/0::check_options/1`  :ref:`classifier_protocol/0::classifier_to_clauses/4`  :ref:`classifier_protocol/0::classifier_to_file/4`  :ref:`options_protocol/0::default_option/1`  :ref:`options_protocol/0::default_options/1`  :ref:`classifier_protocol/0::learn/2`  :ref:`options_protocol/0::option/2`  :ref:`options_protocol/0::option/3`  :ref:`classifier_protocol/0::predict/3`  :ref:`classifier_protocol/0::print_classifier/1`  :ref:`options_protocol/0::valid_option/1`  :ref:`options_protocol/0::valid_options/1`  

.. contents:: :local: :backlinks: top

Public predicates

.. index:: predict/4 .. _knn/0::predict/4:

predict/4 ^^^^^^^^^^^^^

Predicts the class label for a new instance using the learned classifier and the given options. The instance is a list of Attribute-Value pairs.

| Compilation flags: | static

| Template: | predict(Classifier,Instance,Class,Options) | Mode and number of proofs: | predict(+compound,+list,-atom,+list(compound)) - one

---

.. index:: predict_probabilities/3 .. _knn/0::predict_probabilities/3:

predict_probabilities/3 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

Predicts class probabilities for a new instance using the learned classifier and default options. Returns a list of Class-Probability pairs. The instance is a list of Attribute-Value pairs.

| Compilation flags: | static

| Template: | predict_probabilities(Classifier,Instance,Probabilities) | Mode and number of proofs: | predict_probabilities(+compound,+list,-list) - one

---

.. index:: predict_probabilities/4 .. _knn/0::predict_probabilities/4:

predict_probabilities/4 ^^^^^^^^^^^^^^^^^^^^^^^^^^^

Predicts class probabilities for a new instance using the learned classifier and the given options. Returns a list of Class-Probability pairs. The instance is a list of Attribute-Value pairs.

| Compilation flags: | static

| Template: | predict_probabilities(Classifier,Instance,Probabilities,Options) | Mode and number of proofs: | predict_probabilities(+compound,+list,-list,+list(compound)) - one

---

Protected predicates

(no local declarations; see entity ancestors if any)

Private predicates

(no local declarations; see entity ancestors if any)

Operators

(none)

.. seealso::

:ref:`dataset_protocol <dataset_protocol/0>`, :ref:`c45 <c45/0>`, :ref:`isolation_forest <isolation_forest/0>`, :ref:`naive_bayes <naive_bayes/0>`, :ref:`nearest_centroid <nearest_centroid/0>`, :ref:`random_forest <random_forest/0>`, :ref:`ada_boost <ada_boost/0>`