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 continuousThe dataset should not have any missing values Input Any numerical data. Output Predicted classification of a new data point. Statistical Methods used Euclidean DistanceManhattan DistanceMinkowski Distance Limitations It cannot be used on data other than numerical.Since it uses lazy learning, it is slower.

k Nearest Neighbor Regression is located under rubiML ( ) in Regression, in the left task pane. Use the drag-and-drop method to use the algorithm in the canvas. Click the algorithm to view and select different properties for analysis. Refer to Properties of k Nearest Neighbor Regression.

##### Figure: k Nearest Neighbor Regression

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.

## Properties of k Nearest Neighbor Regression

The available properties of k Nearest Neighbor Regression are as shown in the figure given below.

##### Figure: Properties of k Nearest Neighbor Regression

The table given below describes the different fields present on the properties of Lasso Regression.

###### Table: Description of Fields present on the Properties of k Nearest Neighbor Regression
 Field Description Remark Task Name It is the name of the task selected on the workbook canvas. You can click the text field to edit or modify the name of the task as required. Dependent Variable It allows you to select the dependent variable. You can select only one variable, and it should be of numeric type. Independent Variables It allows you to select Independent variables. You can select more than one variable.You can select variables of any type. Advanced Number of Neighbors It allows you to enter the number of neighboring data points to be checked. The default value is 5. Distance Method It allows you to select the method to calculate the distance between two data points. The available options are -Manhattan Distance – The distance between two points measured along axes at right angles. Euclidean Distance – The length of a line segment between two points.The default selection is Euclidean Distance. Minkowski Distance – The distance between two points in the normed vector space. Dimensionality Reduction It allows you to select the dimensionality reduction method. The options are None and PCA.The default value is None.

## Example of k Nearest Neighbor Regression

Consider a Credit Card Balance dataset of people of different gender, age, education, and so on. A snippet of input data is shown in the figure given below.

##### Figure: Input Data Snippet

The table below describes the performance metrics on the result page.

###### Table: Description of Performance Metrics of KNN Regression
 Performance Metric Description Remark RMSE (Root Mean Squared Error) It is the square root of the averaged squared difference between the actual values and the predicted values. It is the most commonly used metric for regression tasks. MAPE (Mean Absolute Percentage Error) It is the average of absolute percentage errors. —

As seen in the above figure, the values for different parameters are –

 Parameter Value RMSE 12.9 MAPE 3.0781

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