# kNN¶

Predict according to the nearest training instances.

**Inputs**

- Data: input dataset
- Preprocessor: preprocessing method(s)

**Outputs**

- Learner: kNN learning algorithm
- Model: trained model

The **kNN** widget uses the kNN algorithm that searches for k closest training examples in feature space and uses their average as prediction.

- A name under which it will appear in other widgets. The default name is “kNN”.
- Set the number of nearest neighbors, the distance parameter (metric) and weights as model criteria.
- Metric can be:
- Euclidean (“straight line”, distance between two points)
- Manhattan (sum of absolute differences of all attributes)
- Maximal (greatest of absolute differences between attributes)
- Mahalanobis (distance between point and distribution).

- The
*Weights*you can use are:**Uniform**: all points in each neighborhood are weighted equally.**Distance**: closer neighbors of a query point have a greater influence than the neighbors further away.

- Metric can be:
- Produce a report.
- When you change one or more settings, you need to click
*Apply*, which will put a new learner on the output and, if the training examples are given, construct a new model and output it as well. Changes can also be applied automatically by clicking the box on the left side of the*Apply*button.

## Examples¶

The first example is a classification task on *iris* dataset. We compare the results of k-Nearest neighbors with the default model Constant, which always predicts the majority class.

The second example is a regression task. This workflow shows how to use the *Learner* output. For the purpose of this example, we used the *housing* dataset. We input the **kNN** prediction model into Predictions and observe the predicted values.