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Diffstat (limited to 'docs/mllib-guide.md')
| -rw-r--r-- | docs/mllib-guide.md | 9 |
1 files changed, 9 insertions, 0 deletions
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index c977bc4f35..1a5c640d10 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -21,6 +21,8 @@ depends on native Fortran routines. You may need to install the if it is not already present on your nodes. MLlib will throw a linking error if it cannot detect these libraries automatically. +To use MLlib in Python, you will also need [NumPy](http://www.numpy.org) version 1.7 or newer. + # Binary Classification Binary classification is a supervised learning problem in which we want to @@ -316,6 +318,13 @@ other signals), you can use the trainImplicit method to get better results. val model = ALS.trainImplicit(ratings, 1, 20, 0.01) {% endhighlight %} +# Using MLLib in Java + +All of MLlib's methods use Java-friendly types, so you can import and call them there the same +way you do in Scala. The only caveat is that the methods take Scala RDD objects, while the +Spark Java API uses a separate `JavaRDD` class. You can convert a Java RDD to a Scala one by +calling `.rdd()` on your `JavaRDD` object. + # Using MLLib in Python Following examples can be tested in the PySpark shell. |