[MINOR][DOC] Fix wrong ml.feature.Normalizer document.

## What changes were proposed in this pull request?
The ```ml.feature.Normalizer``` examples illustrate L1 norm rather than L2, we should correct corresponding document.
![image](https://cloud.githubusercontent.com/assets/1962026/17928637/85aec284-69b0-11e6-9b13-d465ee560581.png)

## How was this patch tested?
Doc change, no test.

Author: Yanbo Liang <ybliang8@gmail.com>

Closes #14787 from yanboliang/normalizer.

docs/ml-features.md

@@ -734,7 +734,7 @@ for more details on the API.
 
 `Normalizer` is a `Transformer` which transforms a dataset of `Vector` rows, normalizing each `Vector` to have unit norm.  It takes parameter `p`, which specifies the [p-norm](http://en.wikipedia.org/wiki/Norm_%28mathematics%29#p-norm) used for normalization.  ($p = 2$ by default.)  This normalization can help standardize your input data and improve the behavior of learning algorithms.
 
-The following example demonstrates how to load a dataset in libsvm format and then normalize each row to have unit $L^2$ norm and unit $L^\infty$ norm.
+The following example demonstrates how to load a dataset in libsvm format and then normalize each row to have unit $L^1$ norm and unit $L^\infty$ norm.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">