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https://issues.apache.org/jira/browse/SPARK-13073?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15210237#comment-15210237
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Samsudhin commented on SPARK-13073:
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@Mohammed Baddar i checked on your comment - 10/Mar/16 13:28
You have executed Linear Regression Summary. For Logistic Regression the
summary would be like below,
> summary(glm(formula = vs ~ wt + hp + gear, family = binomial(), data =
> mtcars))
Call:
glm(formula = vs ~ wt + hp + gear, family = binomial(), data = mtcars)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.79167 -0.19535 -0.00689 0.43289 1.54872
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 11.17572 9.26728 1.206 0.2278
wt 0.55553 1.58811 0.350 0.7265
hp -0.08514 0.03618 -2.353 0.0186 *
gear -0.64723 1.42248 -0.455 0.6491
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 43.86 on 31 degrees of freedom
Residual deviance: 15.89 on 28 degrees of freedom
AIC: 23.89
Number of Fisher Scoring iterations: 7
> 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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