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Mohamed Baddar commented on SPARK-13073: ---------------------------------------- [~josephkb] After looking at source code of org.apache.spark.ml.classification.LogisticRegressionSummary and org.apache.spark.ml.classification.LogisticRegressionTrainingSummary and after running a sample GLM in R which has the following output Call: glm(formula = mpg ~ wt + hp + gear, family = gaussian(), data = mtcars) Deviance Residuals: Min 1Q Median 3Q Max -3.3712 -1.9017 -0.3444 0.9883 6.0655 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 32.013657 4.632264 6.911 1.64e-07 *** wt -3.197811 0.846546 -3.777 0.000761 *** hp -0.036786 0.009891 -3.719 0.000888 *** gear 1.019981 0.851408 1.198 0.240963 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for gaussian family taken to be 6.626347) Null deviance: 1126.05 on 31 degrees of freedom Residual deviance: 185.54 on 28 degrees of freedom AIC: 157.05 Number of Fisher Scoring iterations: 2 I have the following comments : 1-I think we should add the following member to LogisticRegressionSummary : coefficients and residuals 2-toString method should be overridden in the following classes : org.apache.spark.ml.classification.BinaryLogisticRegressionSummary and org.apache.spark.ml.classification.BinaryLogisticRegressionTrainingSummary Any other suggestions ? Please correct me if have missed something. > creating R like summary for logistic Regression in Spark - Scala > ---------------------------------------------------------------- > > Key: SPARK-13073 > URL: https://issues.apache.org/jira/browse/SPARK-13073 > Project: Spark > Issue Type: New Feature > Components: ML, MLlib > Reporter: Samsudhin > Priority: Minor > > Currently Spark ML provides Coefficients for logistic regression. To evaluate > the trained model tests like wald test, chi square tests and their results to > be summarized and display like GLM summary of R -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org