# "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.

# Examples

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## Basic Examples(3)

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:

## Options(1)

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: