SMILE.OUTLIERS(std_deviation_X, columns)
Calculates outliers using the Mahalanobis distance measures. In statistics, Mahalanobis distance is based on correlations between variables by which different patterns can be identified and analyzed. It is a useful way of determining similarity of an unknown sample set to a known one. It differs from the Euclidean distance in that it takes into account the correlations of the data set and is not dependent on the scale of measurements.
Parameters

std_deviation_X – Standard deviation threshold of Mahalanobis distance, after which data point is considered as an outlier; integer (for example, 3).

columns – Dataset columns or custom calculations.
Example: SMILE.OUTLIERS(3, sum([No of customers]), sum([Gross Sales])) used as a calculation for the Color field of the Scatterplot visualization.
Input data
 Numeric variables
 Without missing values
 Size of input data is not limited
Result
 Column of integer values 0 or 1, where 1 is outlier and 0 is inlier.
Key usage points
 It is a multivariate outlier detection method, so multiple variables are allowed.
 Only numeric (continuous) variables are allowed
 Inappropriate for ordinal data
 Calculation of sample covariance matrix makes it selfsensitive to outliers
For the whole list of algorithms, see Data science builtin algorithms.