"ContingencyTable" (Machine Learning Method)

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" Automaticvalue 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)

"AdditiveSmoothing"  (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: