5 files changed, 91 insertions, 35 deletions
diff --git a/docs/ml-features.md b/docs/ml-features.md
index a414c21b5c..b70da4ac63 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -123,12 +123,21 @@ for features_label in rescaledData.select("features", "label").take(3):
 
 ## Word2Vec
 
-`Word2Vec` is an `Estimator` which takes sequences of words that represents documents and trains a `Word2VecModel`. The model is a `Map(String, Vector)` essentially, which maps each word to an unique fix-sized vector. The `Word2VecModel` transforms each documents into a vector using the average of all words in the document, which aims to other computations of documents such as similarity calculation consequencely. Please refer to the [MLlib user guide on Word2Vec](mllib-feature-extraction.html#Word2Vec) for more details on Word2Vec.
+`Word2Vec` is an `Estimator` which takes sequences of words representing documents and trains a
+`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
+details.
 
-Word2Vec is implemented in [Word2Vec](api/scala/index.html#org.apache.spark.ml.feature.Word2Vec). In the following code segment, we start with a set of documents, each of them 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.
+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.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
+
+Refer to the [Word2Vec Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Word2Vec)
+for more details on the API.
+
 {% highlight scala %}
 import org.apache.spark.ml.feature.Word2Vec
 
@@ -152,6 +161,10 @@ result.select("result").take(3).foreach(println)
 </div>
 
 <div data-lang="java" markdown="1">
+
+Refer to the [Word2Vec Java docs](api/java/org/apache/spark/ml/feature/Word2Vec.html)
+for more details on the API.
+
 {% highlight java %}
 import java.util.Arrays;
 
@@ -192,6 +205,10 @@ for (Row r: result.select("result").take(3)) {
 </div>
 
 <div data-lang="python" markdown="1">
+
+Refer to the [Word2Vec Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Word2Vec)
+for more details on the API.
+
 {% highlight python %}
 from pyspark.ml.feature import Word2Vec
 
@@ -621,12 +638,15 @@ for ngrams_label in ngramDataFrame.select("ngrams", "label").take(3):
 
 ## Binarizer
 
-Binarization is the process of thresholding numerical features to binary features. As some probabilistic estimators make assumption that the input data is distributed according to [Bernoulli distribution](http://en.wikipedia.org/wiki/Bernoulli_distribution), a binarizer is useful for pre-processing the input data with continuous numerical features.
+Binarization is the process of thresholding numerical features to binary (0/1) features.
 
-A simple [Binarizer](api/scala/index.html#org.apache.spark.ml.feature.Binarizer) class provides this functionality. Besides the common parameters of `inputCol` and `outputCol`, `Binarizer` has the parameter `threshold` used for binarizing continuous numerical features. The features greater than the threshold, will be binarized to 1.0. The features equal to or less than the threshold, will be binarized to 0.0. The example below shows how to binarize numerical features.
+`Binarizer` takes the common parameters `inputCol` and `outputCol`, as well as the `threshold` for binarization. Feature values greater than the threshold are binarized to 1.0; values equal to or less than the threshold are binarized to 0.0.
 
 <div class="codetabs">
 <div data-lang="scala" markdown="1">
+
+Refer to the [Binarizer API doc](api/scala/index.html#org.apache.spark.ml.feature.Binarizer) for more details.
+
 {% highlight scala %}
 import org.apache.spark.ml.feature.Binarizer
 import org.apache.spark.sql.DataFrame
@@ -650,6 +670,9 @@ binarizedFeatures.collect().foreach(println)
 </div>
 
 <div data-lang="java" markdown="1">
+
+Refer to the [Binarizer API doc](api/java/org/apache/spark/ml/feature/Binarizer.html) for more details.
+
 {% highlight java %}
 import java.util.Arrays;
 
@@ -687,6 +710,9 @@ for (Row r : binarizedFeatures.collect()) {
 </div>
 
 <div data-lang="python" markdown="1">
+
+Refer to the [Binarizer API doc](api/python/pyspark.ml.html#pyspark.ml.feature.Binarizer) for more details.
+
 {% highlight python %}
 from pyspark.ml.feature import Binarizer
 
diff --git a/docs/ml-guide.md b/docs/ml-guide.md
index 78c93a95c7..c5d7f99002 100644
--- a/docs/ml-guide.md
+++ b/docs/ml-guide.md
@@ -32,7 +32,21 @@ See the [algorithm guides](#algorithm-guides) section below for guides on sub-pa
 * This will become a table of contents (this text will be scraped).
 {:toc}
 
-# Main concepts
+# Algorithm guides
+
+We provide several algorithm guides specific to the Pipelines API.
+Several of these algorithms, such as certain feature transformers, are not in the `spark.mllib` API.
+Also, some algorithms have additional capabilities in the `spark.ml` API; e.g., random forests
+provide class probabilities, and linear models provide model summaries.
+
+* [Feature extraction, transformation, and selection](ml-features.html)
+* [Decision Trees for classification and regression](ml-decision-tree.html)
+* [Ensembles](ml-ensembles.html)
+* [Linear methods with elastic net regularization](ml-linear-methods.html)
+* [Multilayer perceptron classifier](ml-ann.html)
+
+
+# Main concepts in Pipelines
 
 Spark ML standardizes APIs for machine learning algorithms to make it easier to combine multiple
 algorithms into a single pipeline, or workflow.
@@ -166,6 +180,11 @@ compile-time type checking.
 `Pipeline`s and `PipelineModel`s instead do runtime checking before actually running the `Pipeline`.
 This type checking is done using the `DataFrame` *schema*, a description of the data types of columns in the `DataFrame`.
 
+*Unique Pipeline stages*: A `Pipeline`'s stages should be unique instances.  E.g., the same instance
+`myHashingTF` should not be inserted into the `Pipeline` twice since `Pipeline` stages must have
+unique IDs.  However, different instances `myHashingTF1` and `myHashingTF2` (both of type `HashingTF`)
+can be put into the same `Pipeline` since different instances will be created with different IDs.
+
 ## Parameters
 
 Spark ML `Estimator`s and `Transformer`s use a uniform API for specifying parameters.
@@ -184,16 +203,6 @@ Parameters belong to specific instances of `Estimator`s and `Transformer`s.
 For example, if we have two `LogisticRegression` instances `lr1` and `lr2`, then we can build a `ParamMap` with both `maxIter` parameters specified: `ParamMap(lr1.maxIter -> 10, lr2.maxIter -> 20)`.
 This is useful if there are two algorithms with the `maxIter` parameter in a `Pipeline`.
 
-# Algorithm guides
-
-There are now several algorithms in the Pipelines API which are not in the `spark.mllib` API, so we link to documentation for them here.  These algorithms are mostly feature transformers, which fit naturally into the `Transformer` abstraction in Pipelines, and ensembles, which fit naturally into the `Estimator` abstraction in the Pipelines.
-
-* [Feature extraction, transformation, and selection](ml-features.html)
-* [Decision Trees for classification and regression](ml-decision-tree.html)
-* [Ensembles](ml-ensembles.html)
-* [Linear methods with elastic net regularization](ml-linear-methods.html)
-* [Multilayer perceptron classifier](ml-ann.html)
-
 # Code examples
 
 This section gives code examples illustrating the functionality discussed above.
diff --git a/docs/mllib-clustering.md b/docs/mllib-clustering.md
index 3fb35d3c50..c2711cf82d 100644
--- a/docs/mllib-clustering.md
+++ b/docs/mllib-clustering.md
@@ -507,6 +507,10 @@ must also be $> 1.0$. Providing `Vector(-1)` results in default behavior
 $> 1.0$. Providing `-1` results in defaulting to a value of $0.1 + 1$.
 * `maxIterations`: The maximum number of EM iterations.
 
+*Note*: It is important to do enough iterations.  In early iterations, EM often has useless topics,
+but those topics improve dramatically after more iterations.  Using at least 20 and possibly
+50-100 iterations is often reasonable, depending on your dataset.
+
 `EMLDAOptimizer` produces a `DistributedLDAModel`, which stores not only
 the inferred topics but also the full training corpus and topic
 distributions for each document in the training corpus. A
diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index de86aba2ae..7e417ed5f3 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -380,35 +380,43 @@ data2 = labels.zip(normalizer2.transform(features))
 </div>
 </div>
 
-## Feature selection
-[Feature selection](http://en.wikipedia.org/wiki/Feature_selection) allows selecting the most relevant features for use in model construction. Feature selection reduces the size of the vector space and, in turn, the complexity of any subsequent operation with vectors. The number of features to select can be tuned using a held-out validation set.
+## ChiSqSelector
 
-### ChiSqSelector
-[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) stands for Chi-Squared feature selection. It operates on labeled data with categorical features. `ChiSqSelector` orders features based on a Chi-Squared test of independence from the class, and then filters (selects) the top features which the class label depends on the most. This is akin to yielding the features with the most predictive power.
+[Feature selection](http://en.wikipedia.org/wiki/Feature_selection) tries to identify relevant
+features for use in model construction. It reduces the size of the feature space, which can improve
+both speed and statistical learning behavior.
 
-#### Model Fitting
+[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) implements
+Chi-Squared feature selection. It operates on labeled data with categorical features.
+`ChiSqSelector` orders features based on a Chi-Squared test of independence from the class,
+and then filters (selects) the top features which the class label depends on the most.
+This is akin to yielding the features with the most predictive power.
 
-[`ChiSqSelector`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) has the
-following parameters in the constructor:
+The number of features to select can be tuned using a held-out validation set.
 
-* `numTopFeatures` number of top features that the selector will select (filter).
+### Model Fitting
 
-We provide a [`fit`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) method in
-`ChiSqSelector` which can take an input of `RDD[LabeledPoint]` with categorical features, learn the summary statistics, and then
-return a `ChiSqSelectorModel` which can transform an input dataset into the reduced feature space.
+`ChiSqSelector` takes a `numTopFeatures` parameter specifying the number of top features that
+the selector will select.
 
-This model implements [`VectorTransformer`](api/scala/index.html#org.apache.spark.mllib.feature.VectorTransformer)
-which can apply the Chi-Squared feature selection on a `Vector` to produce a reduced `Vector` or on
+The [`fit`](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector) method takes
+an input of `RDD[LabeledPoint]` with categorical features, learns the summary statistics, and then
+returns a `ChiSqSelectorModel` which can transform an input dataset into the reduced feature space.
+The `ChiSqSelectorModel` can be applied either to a `Vector` to produce a reduced `Vector`, or to
 an `RDD[Vector]` to produce a reduced `RDD[Vector]`.
 
 Note that the user can also construct a `ChiSqSelectorModel` by hand by providing an array of selected feature indices (which must be sorted in ascending order).
 
-#### Example
+### Example
 
 The following example shows the basic use of ChiSqSelector. The data set used has a feature matrix consisting of greyscale values that vary from 0 to 255 for each feature.
 
 <div class="codetabs">
-<div data-lang="scala">
+<div data-lang="scala" markdown="1">
+
+Refer to the [`ChiSqSelector` Scala docs](api/scala/index.html#org.apache.spark.mllib.feature.ChiSqSelector)
+for details on the API.
+
 {% highlight scala %}
 import org.apache.spark.SparkContext._
 import org.apache.spark.mllib.linalg.Vectors
@@ -434,7 +442,11 @@ val filteredData = discretizedData.map { lp =>
 {% endhighlight %}
 </div>
 
-<div data-lang="java">
+<div data-lang="java" markdown="1">
+
+Refer to the [`ChiSqSelector` Java docs](api/java/org/apache/spark/mllib/feature/ChiSqSelector.html)
+for details on the API.
+
 {% highlight java %}
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaRDD;
@@ -486,7 +498,12 @@ sc.stop();
 
 ## ElementwiseProduct
 
-ElementwiseProduct multiplies each input vector by a provided "weight" vector, using element-wise multiplication. In other words, it scales each column of the dataset by a scalar multiplier.  This represents the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29) between the input vector, `v` and transforming vector, `w`, to yield a result vector.
+`ElementwiseProduct` multiplies each input vector by a provided "weight" vector, using element-wise
+multiplication. In other words, it scales each column of the dataset by a scalar multiplier. This
+represents the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29)
+between the input vector, `v` and transforming vector, `scalingVec`, to yield a result vector.
+Qu8T948*1#
+Denoting the `scalingVec` as "`w`," this transformation may be written as:
 
 `\[ \begin{pmatrix}
 v_1 \\
@@ -506,7 +523,7 @@ v_N
 
 [`ElementwiseProduct`](api/scala/index.html#org.apache.spark.mllib.feature.ElementwiseProduct) has the following parameter in the constructor:
 
-* `w`: the transforming vector.
+* `scalingVec`: the transforming vector.
 
 `ElementwiseProduct` implements [`VectorTransformer`](api/scala/index.html#org.apache.spark.mllib.feature.VectorTransformer) which can apply the weighting on a `Vector` to produce a transformed `Vector` or on an `RDD[Vector]` to produce a transformed `RDD[Vector]`.
 
diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md
index 257f7cc760..91e50ccfec 100644
--- a/docs/mllib-guide.md
+++ b/docs/mllib-guide.md
@@ -13,9 +13,9 @@ primitives and higher-level pipeline APIs.
 
 It divides into two packages:
 
-* [`spark.mllib`](mllib-guide.html#mllib-types-algorithms-and-utilities) contains the original API
+* [`spark.mllib`](mllib-guide.html#data-types-algorithms-and-utilities) contains the original API
   built on top of [RDDs](programming-guide.html#resilient-distributed-datasets-rdds).
-* [`spark.ml`](mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines) provides higher-level API
+* [`spark.ml`](ml-guide.html) provides higher-level API
   built on top of [DataFrames](sql-programming-guide.html#dataframes) for constructing ML pipelines.
 
 Using `spark.ml` is recommended because with DataFrames the API is more versatile and flexible.