knn#
- swordfish.function.knn()#
Implement the k-nearest neighbors (k-NN) algorithm with a brute-force search for classification and regression.
- Parameters:
Y (Constant) – A vector with the same length as the number of rows of X. Each element is a label corresponding to each row in X.
X (Constant) – A table. Each row is an observation and each column is a feature.
type (Constant) – A string. It can be either “regressor” or “classifier”.
nNeighbor (Constant) – A positive integer indicating the number of nearest neighbors in training.
power (Constant) – A positive integer indicating the parameter of Minkowski distance used in training. The default value is 2 indicating Euclidean distance. If power=1, it means Manhattan distance is used in training.
- Returns:
A dictionary with the following keys:
nNeighbor: the number of nearest neighbors in training.
modelName: string “KNN”.
model: the model to be saved.
power: the parameter of Minkowski distance used in training.
type: “regressor” or “classifier”.
- Return type: