Outliers detection algorithms

Use the algorithms for outliers detection to identify the data points that deviate from the overall pattern of your data points. The algorithm assigns a value to the outlier data points, and another value to the inlier data points. For example, the DCPY.LOFOUTLIER algorithm assigns the value '-1' to outliers and the value '1' to inliers.

The following algorithms are available:

• DCPY.LOFOUTLIER(n_neighbors, leaf_size, contamination, columns)
• DCPY.ENVELOPE(contamination, columns)
• DCPY.ISOLATIONFOREST(n_estimators, max_samples, max_features, contamination, columns)
• DCPY.OCSVM(kernel, gamma, degree, coef0, nu, columns)
• SMILE.OUTLIERS(std_deviation_X, columns)

The SCRIPT function can be used to run your custom algorithm based on the available AI connection. For details, see Add script calculations.