# "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:
• 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.

# Examples

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## Basic Examples(3)

Train a contingency-table distribution on a nominal dataset:

 In:= Out= Look at the distribution Information:

 In:= Out= Obtain options information:

 In:= Out= Obtain an option value directly:

 In:= Out= Compute the probabilities for the values "A" and "B":

 In:= Out= In:= Out= Generate new samples:

 In:= Out= Train a contingency-table distribution on a numeric dataset:

 In:= Out= Look at the distribution Information:

 In:= Out= Compute the probability density for a new example:

 In:= Out= Plot the PDF along with the training data:

 In:= Out= Generate and visualize new samples:

 In:= Out= Train a contingency-table distribution on a two-dimensional dataset:

 In:= In:= Out= Plot the PDF along with the training data:

 In:= Out= Use SynthesizeMissingValues to impute missing values using the learned distribution:

 In:= Out= In:= Out= 