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# `discretizer` – Discretization algorithms

Currently, Pebl only includes one discretization implementation but more may come. Discretization and other data pre-processing steps can have a big impact on the final results.

`pebl.discretizer.``maximum_entropy_discretize`(indata, includevars=None, excludevars=[], numbins=3)

  Performs a maximum-entropy discretization of data in-place.

  Requirements for this implementation:

  > > 1. Try to make all bins equal sized (maximize the entropy)
  > > 2. If datum x==y in the original dataset, then disc(x)==disc(y) For example, all datapoints with value 3.245 discretize to 1 even if it violates requirement 1.
  > > 3. Number of bins reflects only the non-missing data.

  > Example:

  > > input: [3,7,4,4,4,5] output: [0,1,0,0,0,1]

  > > Note that all 4s discretize to 0, which makes bin sizes unequal.

  > Example:

  > > input: [1,2,3,4,2,1,2,3,1,x,x,x] output: [0,1,2,2,1,0,1,2,0,0,0,0]

  > > Note that the missing data (‘x’) gets put in the bin with 0.0.

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