Naive Bayes game: Difference between revisions
From Algolit
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by Algolit | by Algolit | ||
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| + | [https://gitlab.constantvzw.org/algolit/mundaneum/tree/master/exhibition/6-Learners/Game_documentation 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. | 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 | + | 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! | 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! | ||
Latest revision as of 18:21, 4 June 2019
by Algolit
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