The Spark documentation shows the following example code:
// Discretize data in 16 equal bins since ChiSqSelector requires categorical
features
val discretizedData = data.map { lp =>
LabeledPoint(lp.label, Vectors.dense(lp.features.toArray.map { x => x / 16
} ) )
}
I'm sort of missing why "x / 16" is considered a discretization approach
here.
[https://spark.apache.org/docs/latest/mllib-feature-extraction.html#feature-selection]
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