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[SPARK-11965] [ML] [Doc] Update user guide for RFormula feature interactions #10222

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20 changes: 19 additions & 1 deletion docs/ml-features.md
Original file line number Diff line number Diff line change
Expand Up @@ -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 meanings of basic operator are:
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I'd say "the basic operators".


* `~` 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
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= -> ~ because the model family is not yet determined

* `y ~ a + b + a:b - 1` means model `y = w1 * a + w2 * b + w3 * a * b` where `w1, w2, w3` are coefficients, which is the same as `y ~ (a + b)^2 + 0`
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  • = -> ~
  • We don't support ^. Mentioning it would confuse users.


`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 string type, it will be first transformed to double label with `StringIndexer`.
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I'd say: "If the label column is of type string, it will be first transformed to double with StringIndexer."

If the label does not already present in the DataFrame, the output label column will be created from the specified response variable in the formula.
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"If the label is not"


**Examples**

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