Object

com.spotify.featran.transformers

MDL

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object MDL extends Serializable

Transform a column of continuous labelled features to n columns of binned categorical features. The optimum number of bins is computed using Minimum Description Length (MDL), which is an entropy measurement between the values and the targets.

The transformer expects an MDLRecord where the first field is a label and the second value is the scalar that will be transformed into buckets.

MDL is an iterative algorithm so all of the data needed to compute the buckets will be pulled into memory. If you run into memory issues the sampleRate parameter should be lowered.

References:

- Fayyad, U., & Irani, K. (1993). "Multi-interval discretization of continuous-valued attributes for classification learning."

- https://github.com/sramirez/spark-MDLP-discretization

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  4. def apply[T](name: String, sampleRate: Double = 1.0, stoppingCriterion: Double = DEFAULT_STOPPING_CRITERION, minBinPercentage: Double = DEFAULT_MIN_BIN_PERCENTAGE, maxBins: Int = DEFAULT_MAX_BINS, seed: Int = Random.nextInt())(implicit arg0: ClassTag[T]): Transformer[MDLRecord[T], B[T], C]

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    Create an MDL Instance.

    Create an MDL Instance.

    sampleRate

    percentage of records to keep to compute the buckets

    stoppingCriterion

    stopping criterion for MDL

    minBinPercentage

    minimum percent of total data allowed in a single bin

    maxBins

    maximum number of thresholds per feature

    seed

    seed for the sampler

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