Github user yanboliang commented on a diff in the pull request: https://github.com/apache/spark/pull/16301#discussion_r92815976 --- 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 -- Nit: ```Multilayer Perceptron``` -> ```Multilayer Perceptron Classifier (MLPC)```
--- If your project is set up for it, you can reply to this email and have your reply appear on GitHub as well. If your project does not have this feature enabled and wishes so, or if the feature is enabled but not working, please contact infrastructure at infrastruct...@apache.org or file a JIRA ticket with INFRA. --- --------------------------------------------------------------------- To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org