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https://issues.apache.org/jira/browse/SPARK-13073?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15189283#comment-15189283
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Mohamed Baddar commented on SPARK-13073:
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[~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
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