[GitHub] [systemml] j143 commented on a change in pull request #962: [DOC] Documentation for builtin cvlm function.

[GitBox <gi...@apache.org>]

j143 commented on a change in pull request #962:
URL: https://github.com/apache/systemml/pull/962#discussion_r436466103



##########
File path: dev/docs/builtins-reference.md
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@@ -322,3 +323,40 @@ y = X %*% rand(rows=ncol(X), 1)
 w = lm(X = X, y = y)
 ress = slicefinder(X = X,W = w, Y = y,  k = 5, paq = 1, S = 2);
 ```
+## `cvlm`-Function
+
+`cvlm` function is used for cross-validation of the provided data model. This function follows Non-exhaustive cross validation  method,
+It uses lm and lmpredict functions to solve the linear regression and to predict the class of a feature vector with no intercept, no shifting, no rescaling. 
+
+### Usage
+```r
+cvlm(X,y,k)
+```
+
+### Arguments
+
+| Name | Type | Default | Description |
+| :--- | :---------- | :-- | :---------- |
+| X | Matrix[Double] | required | Recorded Data set into matrix |
+| y | Matrix[Double] | required | 1-column matrix of response values.  |
+| k | Integer | required | number of subsets needed, It should always be more then 1 and less then nrow(X) |
+| icpt | Integer | 0 | Intercept presence, shifting and rescaling the columns of X |
+| reg | Double | 1e-7 | Regularization constant (lambda) for L2-regularization. set to nonzero for highly dependant/sparse/numerous features |
+
+### Returns
+ 
+| Type | Description |
+| :------------- | :---------- |
+| Matrix[Double] | response values |
+| Matrix[Double] | validated data set |
+
+### Example
+ 
+```r
+X = rand (rows = 5, cols = 5)
+y = X %*% rand(rows = ncol(X), cols = 1)
+print("\n Data set \n"+toString(X)+"\n response values \n"+toString(y))
+[predict,beta]=cvlm(X=X,y=y,k=4)
+print("\n prediction \n"+toString(predict)+"\n validated data set \n"+toString(beta))
+```

Review comment:
       Well done, @wizard-420!
   Running smoothly.




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