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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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-07-13 UTC."],[[["Computes robust least squares regression using iteratively reweighted least squares with the Talwar cost function, handling outliers based on residual error."],["Accepts input tuples with independent and dependent variable values, outputting regression coefficients and root mean square residuals."],["Outputs null if the system is underdetermined (insufficient input data for the number of variables)."],["Offers customization through parameters: `numX` for independent variables, `numY` for dependent variables, and `beta` for outlier detection threshold."]]],[]]