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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 deciding whether a text is positive or negative.  
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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.  
  
    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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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.
  
    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 little caution is needed: because you only train it with 6 sentences - instead of minimum 2000 - it is not representative at all!  
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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 little caution is needed: because you only train it with 6 sentences - instead of minimum 2000 - it is not representative at all!  
  
  
 
[[Category:Data_Workers]][[Category:Data_Workers_EN]]
 
[[Category:Data_Workers]][[Category:Data_Workers_EN]]

Revision as of 18:51, 1 March 2019

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 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.

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 little caution is needed: because you only train it with 6 sentences - instead of minimum 2000 - it is not representative at all!