# Outliers¶

Simple outlier detection by comparing distances between instances.

**Inputs**

- Data: input dataset
- Distances: distance matrix

**Outputs**

- Outliers: instances scored as outliers
- Inliers: instances not scored as outliers

The **Outliers** widget applies one of the two methods for outlier detection. Both methods apply classification to the dataset, one with SVM (multiple kernels) and the other with elliptical envelope. *One-class SVM with non-linear kernels (RBF)* performs well with non-Gaussian distributions, while *Covariance estimator* works only for data with Gaussian distribution.

- Information on the input data, number of inliers and outliers based on the selected model.
- Select the
*Outlier detection method*:**One class SVM with non-linear kernel (RBF)**: classifies data as similar or different from the core class:**Nu**is a parameter for the upper bound on the fraction of training errors and a lower bound of the fraction of support vectors**Kernel coefficient**is a gamma parameter, which specifies how much influence a single data instance has

**Covariance estimator**: fits ellipsis to central points with Mahalanobis distance metric**Contamination**is the proportion of outliers in the dataset**Support fraction**specifies the proportion of points included in the estimate

- Produce a report.
- Click
*Detect outliers*to output the data.

## Example¶

Below, is a simple example of how to use this widget. We used the *Iris* dataset to detect the outliers. We chose the *one class SVM with non-linear kernel (RBF)* method, with Nu set at 20% (less training errors, more support vectors). Then we observed the outliers in the Data Table widget, while we sent the inliers to the Scatter Plot.