k Nearest Neighbor Regression

k Nearest Neighbor Regression

k Nearest Neighbor Regression

Description

k Nearest Neighbor (KNN) Regression enables you to predict new data points based on the known classification of other points. In kNN, we take a bunch of labeled points and then learn how to label other points.

Why to use

To predict the classification of a new data point using data with multiple classes.

When to use

  • When the dataset under consideration is small (less number of data points).
  • When data points are continuous.

When not to use

  • When your dataset contains a large number of data points.
  • When data points are discrete.

Prerequisites

  • Data points should be continuous
  • The dataset should not have any missing values

Input

Any numerical data.

Output

Predicted classification of a new data point.

Statistical Methods used

  • Euclidean Distance
  • Manhattan Distance
  • Minkowski Distance

Limitations

  • It cannot be used on data other than numerical.
  • Since it uses lazy learning, it is slower.

The k-nearest neighbor is a simple and easy-to-use supervised machine learning (ML) algorithm that can be applied to solve regression and classification problems. It assumes that similar things (for example, data points with similar values) exist in proximity. It combines simple mathematical techniques with this similarity to determine the distance between different points on a graph.

The input consists of the k number of training samples that are closest to each other. The output, a class membership, depends on whether the algorithm is being used for regression or classification. In the case of regression, the mean of k labels is returned, while in the case of classification, the mode of k labels is returned.

Classification is done by a vote of majority of the k nearest neighbors, and the new data point is assigned to the class among its k closest neighbors.

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