[DOC] Add documentation for builtin sigmoid func.
Closes #929.
diff --git a/dev/docs/builtins-reference.md b/dev/docs/builtins-reference.md
index b3effb1..3627bd8 100644
--- a/dev/docs/builtins-reference.md
+++ b/dev/docs/builtins-reference.md
@@ -27,9 +27,11 @@
* [`lmDS`-Function](#lmds-function)
* [`lmCG`-Function](#lmcg-function)
* [`lmpredict`-Function](#lmpredict-function)
+ * [`sigmoid`-Function](#sigmoid-function)
* [`steplm`-Function](#steplm-function)
* [`slicefinder`-Function](#slicefinder-function)
+
# Introduction
The DML (Declarative Machine Learning) language has built-in functions which enable access to both low- and high-level functions
@@ -244,6 +246,33 @@
yp = lmpredict(X, w)
```
+## `sigmoid`-Function
+
+The Sigmoid function is a type of activation function, and also defined as a squashing function which limit the output
+to a range between 0 and 1, which will make these functions useful in the prediction of probabilities.
+
+### Usage
+```r
+sigmoid(X)
+```
+
+### Arguments
+| Name | Type | Default | Description |
+| :---- | :------------- | -------- | :---------- |
+| X | Matrix[Double] | required | Matrix of feature vectors. |
+
+
+### Returns
+| Type | Description |
+| :------------- | :---------- |
+| Matrix[Double] | 1-column matrix of weights. |
+
+### Example
+```r
+X = rand (rows = 20, cols = 10)
+Y = sigmoid(X)
+```
+
## `steplm`-Function
The `steplm`-function (stepwise linear regression) implements a classical forward feature selection method.
