skcla_knn: K-Nearest Neighbors Classifier

In daltoolboxdp: Python-Based Extensions for Data Analytics Workflows

K-Nearest Neighbors Classifier

Description

Implements classification using the K-Nearest Neighbors algorithm. This function wraps the KNeighborsClassifier from Python's scikit-learn library.

Usage

skcla_knn(
  attribute,
  slevels,
  n_neighbors = 5,
  weights = "uniform",
  algorithm = "auto",
  leaf_size = 30,
  p = 2,
  metric = "minkowski",
  metric_params = NULL,
  n_jobs = NULL
)

Arguments

attribute	Target attribute name for model building
slevels	List of possible values for classification target
n_neighbors	Number of neighbors to use for queries
weights	Weight function used in prediction ('uniform', 'distance')
algorithm	Algorithm used to compute nearest neighbors ('auto', 'ball_tree', 'kd_tree', 'brute')
leaf_size	Leaf size passed to BallTree or KDTree
p	Power parameter for the Minkowski metric
metric	Distance metric for the tree ('euclidean', 'manhattan', 'chebyshev', 'minkowski', etc.)
metric_params	Additional parameters for the metric function
n_jobs	Number of parallel jobs for neighbor searches

Details

K-Nearest Neighbors Classifier

Value

A K-Nearest Neighbors classifier object

skcla_knn object

Examples

#See an example of using `skcla_knn` at this
#https://github.com/cefet-rj-dal/daltoolboxdp/blob/main/examples/skcla_knn.md

daltoolboxdp documentation built on June 29, 2025, 5:07 p.m.