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Difference between revisions of "Naive Bayes game"

From Algolit

 
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    In machine learning Naive Bayes methods are simple probabilistic classifiers that are widely applied for spam filtering and sentiment analysis.
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by Algolit
  
    They require a small amount of training data to estimate the necessary parameters. They can be extremely fast compared to more sophisticated methods. They are difficult to generalise, this means, that they perform on very specific tasks, demanding to be trained with the same style of data that will be used to work with afterwards.
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[https://gitlab.constantvzw.org/algolit/mundaneum/tree/master/exhibition/6-Learners/Game_documentation Sources on Gitlab]
  
    Naive Bayes is named after Thomas Bayes, a reverend who lived in England in the 18th century. He studied the question that still keeps a lot of people busy these days: what are the chances to win a raffle? The actual formula was invented by Pierre-Simon Laplace, a French mathematician, born in 1749 in Normandy.
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In machine learning Naive Bayes methods are simple probabilistic classifiers that are widely applied for spam filtering and deciding whether a text is positive or negative.  
  
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They require a small amount of training data to estimate the necessary parameters. They can be extremely fast compared to more sophisticated methods. They are difficult to generalize, which means that they perform on specific tasks, demanding to be trained with the same style of data that will be used to work with afterwards.
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This game allows you to play along the rules of Naive Bayes. While manually executing the code, you create your own playful model that 'just works'. A word of caution is necessary: because you only train it with 6 sentences – instead of the minimum 2000 – it is not representative at all!
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Concept & realisation: An Mertens
  
 
[[Category:Data_Workers]][[Category:Data_Workers_EN]]
 
[[Category:Data_Workers]][[Category:Data_Workers_EN]]

Latest revision as of 18:21, 4 June 2019

by Algolit

Sources on Gitlab

In machine learning Naive Bayes methods are simple probabilistic classifiers that are widely applied for spam filtering and deciding whether a text is positive or negative.

They require a small amount of training data to estimate the necessary parameters. They can be extremely fast compared to more sophisticated methods. They are difficult to generalize, which means that they perform on specific tasks, demanding to be trained with the same style of data that will be used to work with afterwards.

This game allows you to play along the rules of Naive Bayes. While manually executing the code, you create your own playful model that 'just works'. A word of caution is necessary: because you only train it with 6 sentences – instead of the minimum 2000 – it is not representative at all!


Concept & realisation: An Mertens