Which of the following is not a parameter of the k-NN algorithm?Number of clustersNumber of neighborsDistance metricWeighting method

To determine which option is not a parameter of the k-Nearest Neighbors (k-NN) algorithm, let's analyze each option provided:

1. Number of clusters: The k-NN algorithm does not involve clustering in the traditional sense. Instead, it is a classification algorithm that considers the 'k' closest neighbors to make predictions. Thus, the concept of “number of clusters” does not apply here.

2. Number of neighbors: This is a fundamental parameter of k-NN. It specifies how many nearby points (neighbors) are considered when making a classification decision.

3. Distance metric: This is also a key parameter in the k-NN algorithm. The distance metric (e.g., Euclidean, Manhattan, etc.) determines how the distance between points is calculated.

4. Weighting method: This parameter is relevant as well. It refers to how the contributions of the neighbors are considered, potentially giving more weight to closer neighbors.

Conclusion

The parameter that is not part of the k-NN algorithm is:

Number of clusters.