[SPARK-11965] [ML] [Doc] Update user guide for RFormula feature interactions

docs/ml-features.md

@@ -2036,7 +2036,25 @@ System.out.println(output.select("userFeatures", "features").first());
 
 ## RFormula
 
-`RFormula` selects columns specified by an [R model formula](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/formula.html). It produces a vector column of features and a double column of labels. Like when formulas are used in R for linear regression, string input columns will be one-hot encoded, and numeric columns will be cast to doubles. If not already present in the DataFrame, the output label column will be created from the specified response variable in the formula.
+`RFormula` selects columns specified by an [R model formula](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/formula.html). 
+Currently we support a limited subset of the R operators, including '~', '.', ':', '+', and '-'.
+The basic operators are:
+
+* `~` separate target and terms
+* `+` concat terms, "+ 0" means removing intercept
+* `-` remove a term, "- 1" means removing intercept
+* `:` interaction (multiplication for numeric values, or binarized categorical values)
+* `.` all columns except target
+
+Suppose `a` and `b` are double columns, we use the following simple examples to illustrate the effect of `RFormula`:
+
+* `y ~ a + b` means model `y ~ w0 + w1 * a + w2 * b` where `w0` is the intercept and `w1, w2` are coefficients.
+* `y ~ a + b + a:b - 1` means model `y ~ w1 * a + w2 * b + w3 * a * b` where `w1, w2, w3` are coefficients.
+
+`RFormula` produces a vector column of features and a double or string column of label. 
+Like when formulas are used in R for linear regression, string input columns will be one-hot encoded, and numeric columns will be cast to doubles.
+If the label column is of type string, it will be first transformed to double with `StringIndexer`.
+If the label column does not exist in the DataFrame, the output label column will be created from the specified response variable in the formula.
 
 **Examples**
 

mllib/src/main/scala/org/apache/spark/ml/feature/RFormula.scala

@@ -45,6 +45,28 @@ private[feature] trait RFormulaBase extends HasFeaturesCol with HasLabelCol {
  * Implements the transforms required for fitting a dataset against an R model formula. Currently
  * we support a limited subset of the R operators, including '~', '.', ':', '+', and '-'. Also see
  * the R formula docs here: http://stat.ethz.ch/R-manual/R-patched/library/stats/html/formula.html
+ *
+ * The basic operators are:
+ *
+ * `~` separate target and terms
+ * `+` concat terms, "+ 0" means removing intercept
+ * `-` remove a term, "- 1" means removing intercept
+ * `:` interaction (multiplication for numeric values, or binarized categorical values)
+ * `.` all columns except target
+ *
+ * Suppose `a` and `b` are double columns, we use the following simple examples
+ * to illustrate the effect of `RFormula`:
+ * `y ~ a + b` means model `y ~ w0 + w1 * a + w2 * b` where `w0` is the intercept and `w1, w2`
+ * are coefficients.
+ * `y ~ a + b + a:b - 1` means model `y ~ w1 * a + w2 * b + w3 * a * b` where `w1, w2, w3`
+ * are coefficients.
+ *
+ * RFormula produces a vector column of features and a double or string column of label.
+ * Like when formulas are used in R for linear regression, string input columns will be one-hot
+ * encoded, and numeric columns will be cast to doubles.
+ * If the label column is of type string, it will be first transformed to double with
+ * `StringIndexer`. If the label column does not exist in the DataFrame, the output label column
+ * will be created from the specified response variable in the formula.
  */
 @Experimental
 class RFormula(override val uid: String) extends Estimator[RFormulaModel] with RFormulaBase {