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new 8aed1e9 [DOC] Documentation for builtin confusionMatrix function
8aed1e9 is described below
commit 8aed1e98f616b3e65ffea4f2d1b54fce6c2ab3e5
Author: Supratick Dey <[email protected]>
AuthorDate: Mon Jun 8 14:29:46 2020 +0530
[DOC] Documentation for builtin confusionMatrix function
Closes #952.
---
dev/docs/builtins-reference.md | 35 +++++++++++++++++++++++++++++++++++
1 file changed, 35 insertions(+)
diff --git a/dev/docs/builtins-reference.md b/dev/docs/builtins-reference.md
index dca37e5..ecbdf13 100644
--- a/dev/docs/builtins-reference.md
+++ b/dev/docs/builtins-reference.md
@@ -23,6 +23,7 @@ limitations under the License.
* [Built-In Construction Functions](#built-in-construction-functions)
* [`tensor`-Function](#tensor-function)
* [DML-Bodied Built-In functions](#dml-bodied-built-in-functions)
+ * [`confusionMatrix`-Function](#confusionmatrix-function)
* [`gridSearch`-Function](#gridSearch-function)
* [`KMeans`-Function](#KMeans-function)
* [`lm`-Function](#lm-function)
@@ -119,6 +120,40 @@ Note that reshape construction is not yet supported for
**SPARK** execution.
**DML-bodied built-in functions** are written as DML-Scripts and executed as
such when called.
+## `confusionMatrix`-Function
+
+A `confusionMatrix`-accepts a vector for prediction and a one-hot-encoded
matrix, then it computes the max value
+of each vector and compare them, after which it calculates and returns the sum
of classifications and the average of
+each true class.
+
+### Usage
+```r
+confusionMatrix(P, Y)
+```
+
+### Arguments
+
+| Name | Type | Default | Description |
+| :--- | :------------- | :------ | :---------- |
+| P | Matrix[Double] | --- | vector of prediction |
+| Y | Matrix[Double] | --- | vector of Golden standard One Hot Encoded |
+
+### Returns
+
+| Name | Type | Description |
+| :----------- | :------------- | :---------- |
+| ConfusionSum | Matrix[Double] | The Confusion Matrix Sums of classifications
|
+| ConfusionAvg | Matrix[Double] | The Confusion Matrix averages of each true
class |
+
+### Example
+
+```r
+numClasses = 1
+z = rand(rows = 5, cols = 1, min = 1, max = 9)
+X = round(rand(rows = 5, cols = 1, min = 1, max = numClasses))
+y = toOneHot(X, numClasses)
+[ConfusionSum, ConfusionAvg] = confusionMatrix(P=z, Y=y)
+```
## `gridSearch`-Function
