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Iterative Reweighted Least Squares (IRLS) is a numerical method for solving logistic regression by approximating the maximum likelihood estimates of the parameters.
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Unlike ordinary least squares used in linear regression, logistic regression involves a nonlinear relationship, so IRLS works iteratively. At each step, it "reweights" the data based on the current predictions and solves a weighted least squares problem.
This process continues until the parameters converge to a stable solution. IRLS is particularly effective for fitting logistic regression models as it combines efficiency with accuracy.
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