Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/16301#discussion_r92879246
--- Diff: R/pkg/vignettes/sparkr-vignettes.Rmd ---
@@ -496,9 +508,114 @@ count(carsDF_test)
head(carsDF_test)
```
-
### Models and Algorithms
+#### Logistic Regression Model
+
+[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 Predictive
Model](https://en.wikipedia.org/wiki/Generalized_linear_model).
+We provide `spark.logit` on top of `spark.glm` to support logistic
regression with advanced hyper-parameters.
+It supports both binary and multiclass classification with elastic-net
regularization and feature standardization, similar to `glmnet`.
+
+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 = 0.00042)
+summary(model)
+```
+
+Predict values on training data
+```{r}
+fitted <- predict(model, training)
+```
+
+Multinomial logistic regression against three classes
+```{r, warning=FALSE}
+df <- createDataFrame(iris)
+# Note in this case, Spark infers it is multinomial logistic regression,
so family = "multinomial" is optional.
+model <- spark.logit(df, Species ~ ., regParam = 0.056)
+summary(model)
+```
+
+#### Multilayer Perceptron
--- End diff --
+1 on removing `model` at the end
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