K Nearest Neighbour
k Nearest Neighbor Regression is located under Machine Learning (
) 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.
Properties of k Nearest Neighbor Regression
The available properties of k Nearest Neighbor Regression are as shown in the figure given below.
The table given below describes the different fields present on the properties of Lasso Regression.
Field | Description | Remark | |
| Run | It allows you to run the node. | - | |
| Explore | It allows you to explore the successfully executed node. | - | |
| Vertical Ellipses | The available options are
| - | |
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. |
| |
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 -
| |
Dimensionality Reduction | It allows you to select the dimensionality reduction method. |
| |
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.
The table below describes the performance metrics on the result page.
| Performance Metric | Descripton | 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 performance metric of the model. |
R Square | It is the statistical measure that determines the proportion of variance in the dependent variable that is explained by the independent variables. | Value is always between 0 and 1. |
Adjusted R Square | It is an improvement of R Square. It adjusts for the increasing predictors and only shows improvement if there is a real improvement. | Adjusted R Square is always lower than R Square. |
AIC (Akaike Information Criterion) | AIC is an estimator of errors in predicted values and signifies the quality of the model for a given dataset. | A model with the least AIC is preferred. |
BIC | BIC is a criterion for model selection amongst a finite set of models. | A model with the least BIC is preferred. |
| MSE (Mean Squared Error) | It is the averaged squared difference between the actual values and the predicted values. | A model with low MSE is preferred. |
| MAE (Mean Absolute Error) | It the absolute value of difference between actual and predicted values | A model with low MAE is preferred. |
| MAPE ( Mean Absolute Percentage Error) | it is the average magnitude of error produced by a model, or how far off predictions are on average. | A model with low MAPE is preferred |
As seen in the above figure, the values for different parameters are –
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