KNeighborsClassifier
K-Nearest Neighbors Classifier. Classification based on k nearest neighbors using majority vote.
Instance-based learning algorithm:
- Store all training data
- For each test sample, find k nearest training samples
- Predict class by majority vote (or weighted vote)
Time Complexity:
- Training: O(1) (just stores data)
- Prediction: O(n * d) per sample where n=training samples, d=features
Constructor
new KNeighborsClassifier(options?: {
nNeighbors?: number;
weights?: "uniform" | "distance";
metric?: "euclidean" | "manhattan";
})
Number of neighbors to use for prediction.
Weight function: ‘uniform’ (all neighbors weighted equally) or ‘distance’ (closer neighbors weighted more).
options.metric
string
default:"euclidean"
Distance metric: ‘euclidean’ or ‘manhattan’.
Methods
fit
fit(X: Tensor, y: Tensor): this
Training data of shape (n_samples, n_features)
Target class labels of shape (n_samples,)
InvalidParameterError if nNeighbors > n_samples
predict
predict(X: Tensor): Tensor
predictProba
predictProba(X: Tensor): Tensor
score
score(X: Tensor, y: Tensor): number
Example
import { KNeighborsClassifier } from 'deepbox/ml';
import { tensor } from 'deepbox/ndarray';
const X = tensor([[0, 0], [1, 1], [2, 2], [3, 3]]);
const y = tensor([0, 0, 1, 1]);
const knn = new KNeighborsClassifier({ nNeighbors: 3 });
knn.fit(X, y);
const predictions = knn.predict(tensor([[1.5, 1.5]]));
KNeighborsRegressor
K-Nearest Neighbors Regressor. Predicts value as mean (or weighted mean) of k nearest training samples.
Constructor
new KNeighborsRegressor(options?: {
nNeighbors?: number;
weights?: "uniform" | "distance";
metric?: "euclidean" | "manhattan";
})
Number of neighbors to use.
Weight function: ‘uniform’ or ‘distance’.
options.metric
string
default:"euclidean"
Distance metric: ‘euclidean’ or ‘manhattan’.
Methods
fit
fit(X: Tensor, y: Tensor): this
Training data of shape (n_samples, n_features)
Target values of shape (n_samples,)
predict
predict(X: Tensor): Tensor
score
score(X: Tensor, y: Tensor): number
Example
import { KNeighborsRegressor } from 'deepbox/ml';
import { tensor } from 'deepbox/ndarray';
const X = tensor([[0], [1], [2], [3]]);
const y = tensor([0, 1, 4, 9]);
const knn = new KNeighborsRegressor({ nNeighbors: 2 });
knn.fit(X, y);
const predictions = knn.predict(tensor([[1.5]]));