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In R, model fitting comes with summary statistics. We can provide most of those via normal equation solver (SPARK9834). If some statistics requires additional passes to the dataset, we can expose an option to let users select desired statistics before model fitting.
> summary(model) Call: glm(formula = Sepal.Length ~ Sepal.Width + Species, data = iris) Deviance Residuals: Min 1Q Median 3Q Max 1.30711 0.25713 0.05325 0.19542 1.41253 Coefficients: Estimate Std. Error t value Pr(>t) (Intercept) 2.2514 0.3698 6.089 9.57e09 *** Sepal.Width 0.8036 0.1063 7.557 4.19e12 *** Speciesversicolor 1.4587 0.1121 13.012 < 2e16 *** Speciesvirginica 1.9468 0.1000 19.465 < 2e16 ***  Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for gaussian family taken to be 0.1918059) Null deviance: 102.168 on 149 degrees of freedom Residual deviance: 28.004 on 146 degrees of freedom AIC: 183.94 Number of Fisher Scoring iterations: 2
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SPARK9837 Provide Rlike summary statistics for GLMs via iteratively reweighted least squares
 Resolved
 is blocked by

SPARK10668 Use WeightedLeastSquares in LinearRegression with L2 regularization if the number of features is small
 Resolved

SPARK9834 Normal equation solver for ordinary least squares
 Resolved
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Hello , Can i be assigned to This Task
Thanks