diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md
index 4288ebc03895c..a69e41e2a1936 100644
--- a/docs/mllib-feature-extraction.md
+++ b/docs/mllib-feature-extraction.md
@@ -405,7 +405,7 @@ Note that the user can also construct a `ChiSqSelectorModel` by hand by providin
#### 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 - 255 for each feature.
+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.
@@ -423,7 +423,7 @@ val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt")
val discretizedData = data.map { lp =>
LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x => (x / 16).floor } ) )
}
-// Create ChiSqSelector that will select top 50 of 692 features
+// Create ChiSqSelector that will select top 50 of 692 features
val selector = new ChiSqSelector(50)
// Create ChiSqSelector model (selecting features)
val transformer = selector.fit(discretizedData)
@@ -459,13 +459,13 @@ JavaRDD discretizedData = points.map(
public LabeledPoint call(LabeledPoint lp) {
final double[] discretizedFeatures = new double[lp.features().size()];
for (int i = 0; i < lp.features().size(); ++i) {
- discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16);
+ discretizedFeatures[i] = Math.floor(lp.features().apply(i) / 16);
}
return new LabeledPoint(lp.label(), Vectors.dense(discretizedFeatures));
}
});
-// Create ChiSqSelector that will select top 50 of 692 features
+// Create ChiSqSelector that will select top 50 of 692 features
ChiSqSelector selector = new ChiSqSelector(50);
// Create ChiSqSelector model (selecting features)
final ChiSqSelectorModel transformer = selector.fit(discretizedData.rdd());