knnregressorfit

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

Syntax

parameters = knnregressorfit(X,y)

parameters = knnregressorfit(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' (default): All neighbors are weighted equally; '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. 'ball_tree': uses Ball Tree to search for neighbors; 'kd_tree': uses KD Tree to search for neighbors; 'brute': uses Brute Force search; 'auto' (default): decides the most appropriate algorithm based on the input.
Type: char
Dimension: string
leaf_size
Leaf sized passed to Ball Tree or KD Tree if they are chosen as the algorithm (default: 30).
Type: integer
Dimension: scalar
p
Represents Lp norm in minkowski distance (default: 2, which is the L2 norm which is Euclidean distance).
Type: double | 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 knnregressorfit method as options. Additionally it has below key-value pairs.
Type: struct
scorer
Function handle pointing to r2 function (R2 Coefficient of Determination).
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 knnregressorfit with options

X = [0; 1; 2; 3];
y = [0; 0; 1; 1];
options = struct;
options.n_neighbors = 2;
parameters = knnregressorfit(X, y, options)
parameters = struct [
algorithm: auto
leaf_size: 30
metric: minkowski
model_name: model_16353122674771364
n_features: 1
n_neighbors: 2
n_samples: 4
p: 2
weights: uniform
]

Comments

It performs regression based on K Nearest Neighbors algorithm. Once the neighbors are found, target is predicted using aggregation of nearest neighbors. Output 'parameters' can be passed to knnregressorpredict function.