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Creates a reducer that computes a robust least squares regression with numX independent variables and numY dependent variables, using iteratively reweighted least squares with the Talwar cost function. A point is considered an outlier if the RMS of residuals is greater than beta.

Each input tuple will have values for the independent variables followed by the dependent variables.

The first output is a coefficients array with dimensions (numX, numY); each column contains the coefficients for the corresponding dependent variable. The second is a vector of the root mean square of the residuals of each dependent variable. Both outputs are null if the system is underdetermined, e.g. the number of inputs is less than numX.

ee.Reducer.robustLinearRegression(numX, numY, beta)Reducer
numXIntegerThe number of input dimensions.
numYInteger, default: 1The number of output dimensions.
betaFloat, default: nullResidual error outlier margin. If null, a default value will be computed.