skcla_knn {daltoolboxdp}R Documentation

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

[Package daltoolboxdp version 1.2.707 Index]