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| -rw-r--r-- | docs/ml-features.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/docs/ml-features.md b/docs/ml-features.md index 6020114845..e41bf78521 100644 --- a/docs/ml-features.md +++ b/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"> |