Easy

k-Nearest Neighbors Classifier

~20 minimplementation

Implement knn_predict(X_train, y_train, X_test, k).

  • X_train has shape (n_train, n_features), X_test has shape (n_test, n_features).
  • y_train is a 1D array of integer labels.
  • Return a 1D array of n_test predicted labels.

Hint: broadcasting gives you the whole distance matrix in one line: X_test[:, None, :] - X_train[None, :, :]. You never need to rank distances, but it costs nothing here.

Examples

k=3, two well-separated clusters: point near the origin cluster

Input
knn_predict([[0, 0], [1, 0], [0, 1], [5, 5], [6, 5]], [0, 0, 0, 1, 1], [[0.5, 0.5]], 3)
Output
[0]

k=1 reduces to nearest-neighbor lookup

Input
knn_predict([[0, 0], [1, 0], [0, 1], [5, 5], [6, 5]], [0, 0, 0, 1, 1], [[5.2, 5.1], [0.1, 0.2]], 1)
Output
[1, 0]

Larger k pulls in the majority class across the boundary

Input
knn_predict([[0, 0], [1, 0], [0, 1], [5, 5], [6, 5]], [0, 0, 0, 1, 1], [[4, 4]], 5)
Output
[0]

Hints

Hint 1

You need the index of the extreme value, not the value itself.

Hint 2

Watch for this: used squared distance but compared against a sqrt threshold.

Requirements

  • X_train: (n_train, n_features) training features

  • y_train: (n_train,) integer labels

  • X_test: (n_test, n_features) points to classify

  • k: number of neighbors to consider

  • Return (n_test,) array of predicted integer labels.

  • Use a fully vectorised implementation without Python loops

Constraints

  • Vectorised implementation only, no Python loops

  • Allowed library: NumPy only

  • Time limit: 200 ms, Memory: 64 MB

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Where this shows up

~20 min

7 employers weight this skill

2 health and bio companies, 2 quant funds, 2 enterprise vendors, 1 AI product company. Top match scores 75.

Python
import numpy as np

def knn_predict(X_train: np.ndarray, y_train: np.ndarray,
                X_test: np.ndarray, k: int) -> np.ndarray:
    """
    Predict labels for X_test by majority vote over the k nearest
    training points under Euclidean distance.

    Ties in the vote are broken by choosing the smallest label.

    Args:
        X_train: (n_train, n_features) training features
        y_train: (n_train,) integer labels
        X_test:  (n_test, n_features) points to classify
        k:       number of neighbors to consider

    Returns:
        (n_test,) array of predicted integer labels.
    """
    # YOUR CODE HERE
    pass

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