azagrebin commented on a change in pull request #6425: [FLINK-9664][Doc] fixing
documentation in ML quick start
URL: https://github.com/apache/flink/pull/6425#discussion_r209894118
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File path: docs/dev/libs/ml/quickstart.md
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@@ -146,7 +145,23 @@ create a classifier.
## Classification
-Once we have imported the dataset we can train a `Predictor` such as a linear
SVM classifier.
+After importing the training and test dataset, they need to be prepared for
the classification.
+Because Flink SVM only supports threshold binary values of `+1.0` and `-1.0`,
a conversion is
+needed after loading the LibSVM dataset since it is labelled using `1`s and
`0`s.
+
+A conversion can be done using a simple normalizer mapping function:
+
+{% highlight scala %}
+
+def normalizer : LabeledVector => LabeledVector = {
+ lv => LabeledVector(if (lv.label > 0.0) 1.0 else -1.0, lv.vector)
+}
+val astroTrain: DataSet[LabeledVector] = astroTrainLibSVM.map(normalizer)
+val astroTest: DataSet[(Vector, Double)] =
astroTestLibSVM.map(normalizer).map(x => (x.vector, x.label))
+
+{% endhighlight %}
+
+Once we have the converted the dataset we can train a `Predictor` such as a
linear SVM classifier.
Review comment:
One superfluous `the`:
Once we have the converted **the** dataset
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