diff --git a/docs/ml-features.md b/docs/ml-features.md index 8b00cc652d..158f3f2018 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -63,7 +63,7 @@ the [IDF Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.IDF) for mor `Word2VecModel`. The model maps each word to a unique fixed-size vector. The `Word2VecModel` transforms each document into a vector using the average of all words in the document; this vector can then be used for as features for prediction, document similarity calculations, etc. -Please refer to the [MLlib user guide on Word2Vec](mllib-feature-extraction.html#Word2Vec) for more +Please refer to the [MLlib user guide on Word2Vec](mllib-feature-extraction.html#word2Vec) for more details. In the following code segment, we start with a set of documents, each of which is represented as a sequence of words. For each document, we transform it into a feature vector. This feature vector could then be passed to a learning algorithm. @@ -411,7 +411,7 @@ for more details on the API. Refer to the [DCT Java docs](api/java/org/apache/spark/ml/feature/DCT.html) for more details on the API. -{% include_example java/org/apache/spark/examples/ml/JavaDCTExample.java %}} +{% include_example java/org/apache/spark/examples/ml/JavaDCTExample.java %} @@ -669,7 +669,7 @@ for more details on the API. 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.