knnclassifierfit

Trains the K Nearest Neighbors Classifier from the training dataset and computes the required parameters to be used by knnClassifierPredict method for making predictions.

Attention: Available only with Activate commercial edition.

Syntax

parameters = knnclassifierfit(X,y)

parameters = knnclassifierfit(X,y,options)

Inputs

X
Training data.
Type: double
Dimension: vector | matrix
y
Target values.
Type: double
Dimension: vector | matrix
options
Type: struct
n_neighbors
Number of neighbors to use while making prediction (default: 5).
Type: integer
Dimension: scalar
weights
Weight function used in prediction. 'uniform': all neighbors are weighted equally (default); 'distance': all neighbors are weighted by the inverse of their distance between query point and them. Greater the distance, lesser the weight for the neighbour.
Type: char
Dimension: string
algorithm
Algorithm to compute the nearest neighbors. 'auto': decides the most appropriate algorithm based on the input (default), 'ball_tree': uses Ball Tree to search for neighbors; 'kd_tree': uses KD Tree to search for neighbors; 'brute': uses Brute Force search.
Type: char
Dimension: string
leaf_size
Leaf sized passed to Ball Tree or KD Tree if they’re chosen as the algorithm (default: 30).
Type: integer
Dimension: scalar
p
p represents Lp norm in minkowski distance. Default: 2 (represents L2 norm which is Euclidean distance)
Type: integer
Dimension: scalar
metric
Distance metric to compute distance between data points. 'chebyshev', 'cityblock', 'euclidean', 'infinity', 'l1', 'l2', 'manhattan', 'minkowski' (default with p = 2)
Type: char
Dimension: string

Outputs

parameters
Contains all the values passed to knnclassifierfit method as options. Additionally it has below key-value pairs.
Type: struct
scorer
Function handle pointing to 'accuracy' function.
Type: function handle
n_samples
Number of rows in the training data.
Type: integer
Dimension: scalar
n_features
Number of columns in the training data.
Type: integer
Dimension: scalar

Example

Usage of knnclassifierfit

data = dlmread('digits.csv', ',');
X = data(:, 1:end-1);
y = data(:, end);

parameters = knnclassifierfit(X, y);
> parameters
parameters = struct [
  algorithm: auto
  leaf_size: 30
  metric: minkowski
  n_features: 64
  n_neighbors: 5
  n_samples: 1797
  p: 2
  weights: uniform
]

Comments

Output 'parameters' should be passed as input to knnclassifierpredict function.