[SPARK-18088][ML] Various ChiSqSelector cleanups

## What changes were proposed in this pull request?
- Renamed kbest to numTopFeatures
- Renamed alpha to fpr
- Added missing Since annotations
- Doc cleanups
## How was this patch tested?

Added new standardized unit tests for spark.ml.
Improved existing unit test coverage a bit.

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #15647 from jkbradley/chisqselector-follow-ups.

2 files changed, 12 insertions, 15 deletions

diff --git a/docs/ml-features.md b/docs/ml-features.md
index 64c6a16023..352887d3ba 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1338,14 +1338,14 @@ for more details on the API.
 `ChiSqSelector` stands for Chi-Squared feature selection. It operates on labeled data with
 categorical features. ChiSqSelector uses the
 [Chi-Squared test of independence](https://en.wikipedia.org/wiki/Chi-squared_test) to decide which
-features to choose. It supports three selection methods: `KBest`, `Percentile` and `FPR`:
+features to choose. It supports three selection methods: `numTopFeatures`, `percentile`, `fpr`:
 
-* `KBest` chooses the `k` top features according to a chi-squared test. This is akin to yielding the features with the most predictive power.
-* `Percentile` is similar to `KBest` but chooses a fraction of all features instead of a fixed number.
-* `FPR` chooses all features whose false positive rate meets some threshold.
+* `numTopFeatures` chooses a fixed number of top features according to a chi-squared test. This is akin to yielding the features with the most predictive power.
+* `percentile` is similar to `numTopFeatures` but chooses a fraction of all features instead of a fixed number.
+* `fpr` chooses all features whose p-value is below a threshold, thus controlling the false positive rate of selection.
 
-By default, the selection method is `KBest`, the default number of top features is 50. User can use
-`setNumTopFeatures`, `setPercentile` and `setAlpha` to set different selection methods.
+By default, the selection method is `numTopFeatures`, with the default number of top features set to 50.
+The user can choose a selection method using `setSelectorType`.
 
 **Examples**
 
diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 87e1e027e9..42568c312e 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -227,22 +227,19 @@ both speed and statistical learning behavior.
 [`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 uses the
 [Chi-Squared test of independence](https://en.wikipedia.org/wiki/Chi-squared_test) to decide which
-features to choose. It supports three selection methods: `KBest`, `Percentile` and `FPR`:
+features to choose. It supports three selection methods: `numTopFeatures`, `percentile`, `fpr`:
 
-* `KBest` chooses the `k` top features according to a chi-squared test. This is akin to yielding the features with the most predictive power.
-* `Percentile` is similar to `KBest` but chooses a fraction of all features instead of a fixed number.
-* `FPR` chooses all features whose false positive rate meets some threshold.
+* `numTopFeatures` chooses a fixed number of top features according to a chi-squared test. This is akin to yielding the features with the most predictive power.
+* `percentile` is similar to `numTopFeatures` but chooses a fraction of all features instead of a fixed number.
+* `fpr` chooses all features whose p-value is below a threshold, thus controlling the false positive rate of selection.
 
-By default, the selection method is `KBest`, the default number of top features is 50. User can use
-`setNumTopFeatures`, `setPercentile` and `setAlpha` to set different selection methods.
+By default, the selection method is `numTopFeatures`, with the default number of top features set to 50.
+The user can choose a selection method using `setSelectorType`.
 
 The number of features to select can be tuned using a held-out validation set.
 
 ### Model Fitting
 
-`ChiSqSelector` takes a `numTopFeatures` parameter specifying the number of top features that
-the selector will select.
-
 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.