Github user mengxr commented on a diff in the pull request: https://github.com/apache/spark/pull/16222#discussion_r91822509 --- Diff: R/pkg/vignettes/sparkr-vignettes.Rmd --- @@ -768,8 +768,46 @@ newDF <- createDataFrame(data.frame(x = c(1.5, 3.2))) head(predict(isoregModel, newDF)) ``` -#### What's More? -We also expect Decision Tree, Random Forest, Kolmogorov-Smirnov Test coming in the next version 2.1.0. +### Logistic Regression Model + +(Added in 2.1.0) + +[Logistic regression](https://en.wikipedia.org/wiki/Logistic_regression) is a widely-used model when the response is categorical. It can be seen as a special case of the [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model). +There are two types of logistic regression models, namely binomial logistic regression (i.e., response is binary) and multinomial +logistic regression (i.e., response falls into multiple classes). We provide `spark.logit` on top of `spark.glm` to support logistic regression with advanced hyper-parameters. +It supports both binary and multiclass classification, elastic-net regularization, and feature standardization, similar to `glmnet`. --- End diff -- to be consistent with the text, we can say "both binomial and multinomial logistic regression models with elastic-net regularization and feature standardization, similar ..."
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