Trains the K Nearest Neighbors Regressor from the training dataset and computes the required parameters to be used by knnregressorpredict method for making predictions.
Attention: Available only with Activate commercial edition.
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 keyvalue 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.