Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/16222#discussion_r91823358
--- 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`.
+
+
+`spark.logit` fits an Logistic Regression Model against a Spark DataFrame.
The `family` parameter can be used to select between the
+binomial and multinomial algorithms, or leave it unset and Spark will
infer the correct variant.
+
+We use a simple example to demonstrate `spark.logit` usage. In general,
there are three steps of using `spark.logit`:
+1). Create a dataframe from a proper data source; 2). Fit a logistic
regression model using `spark.logit` with a proper parameter setting;
+and 3). Obtain the coefficient matrix of the fitted model using `summary`
and use the model for prediction with `predict`.
+
+Binomial logistic regression
+```{r, warning=FALSE}
+df <- createDataFrame(iris)
+# Create a dataframe containing two classes
+training <- df[df$Species %in% c("versicolor", "virginica"), ]
+model <- spark.logit(training, Species ~ ., regParam = 0.5)
+summary <- summary(model)
+head(summary)
+```
+
+Predict values on training data
+```{r}
+fitted <- predict(model, training)
+```
+
+Multinomial logistic regression against three classes
+```{r, warning=FALSE}
+df <- createDataFrame(iris)
+# Note family = "multinomial" is optional in this case since the dataset
has multiple classes.
--- End diff --
This reads like `family = "binomial"` is required if the dataset has only
two classes.
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