Showing posts with label statistical techniques. Show all posts
Showing posts with label statistical techniques. Show all posts

AI Glossary - What Is Arcing?


Arcing methods are a broad category of Adaptive Resampling and Combining approaches for boosting machine learning and statistical techniques' performance.

ADABOOST and bagging are two prominent examples.

In general, these strategies apply a learning technique to a training set repeatedly, such as a decision tree, and then reweight, or resample, the data and refit the learning technique to the data.

This results in a set of learning rules.

New observations are passed through all members of the collection, and the predictions or classifications are aggregated by averaging or a majority rule prediction to generate a combined result.

These strategies may provide findings that are significantly more accurate than a single classifier, but being less interpretable than a single classifier.

They can build minimum (Bayes) risk classifiers, according to research.

See Also: 

ADABOOST, Bootstrap AGGregation

~ Jai Krishna Ponnappan

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