"ContingencyTable" (Machine Learning Method)

Method for LearnDistribution.
Use a table to store the probabilities of a nominal vector for each possible outcome.

Details & Suboptions

A contingency table models the probability distribution of a nominal vector space by storing a probability value for each possible outcome.
If the data is unidimensional, the distribution corresponds to a categorical distribution.
The following options can be given:
"AdditiveSmoothing" Automatic value to be added to each count
If the data contains numerical values, they are discretized. The resulting distribution is still a valid distribution in the original space.
Information[LearnedDistribution[…],"MethodOption"] can be used to extract the values of options chosen by the automation system.
LearnDistribution[…,FeatureExtractor"Minimal"] can be used to remove most preprocessing and directly access the method.

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Train a contingency-table distribution on a nominal dataset:

Look at the distribution Information:

Obtain options information:

Obtain an option value directly:

Compute the probabilities for the values "A" and "B":

Generate new samples:

Train a contingency-table distribution on a numeric dataset:

Look at the distribution Information:

Compute the probability density for a new example:

Plot the PDF along with the training data:

Generate and visualize new samples:

Train a contingency-table distribution on a two-dimensional dataset:

Plot the PDF along with the training data:

Use SynthesizeMissingValues to impute missing values using the learned distribution:

Train a contingency-table distribution on a nominal dataset without any smoothing:

Compute the probabilities for the values "A" and "B":

Compare with the probabilities obtained after adding 1 and 10 counts to each outcome:

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