Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from http://reference.wolfram.com/language/ref/method/ContingencyTable.html below:

ContingencyTable—Wolfram Language Documentation

We deliver solutions for the AI era—combining symbolic computation, data-driven insights and deep technology expertise.

• Data & Computational Intelligence
• Model-Based Design
• Algorithm Development
• Wolfram|Alpha for Business
• Blockchain Technology
• Education Technology
• Quantum Computation

WolframConsulting.com

• See Also
  • LearnDistribution
  • LearnedDistribution
  • AnomalyDetection
  • SynthesizeMissingValues
  • RandomVariate
  • PDF
  • RarerProbability
  • Methods
  • DecisionTree
  • KernelDensityEstimation
  • NaiveBayes

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

• 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.

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:

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4