Difference between revisions of "...predict categories"

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**** linear, then try the [[Bayes#Naive Bayes|Naive Bayes]]   
 
**** linear, then try the [[Bayes#Naive Bayes|Naive Bayes]]   
 
**** accurate, can allow long training times, then try the [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]] e.g. [[Image Classification]]
 
**** accurate, can allow long training times, then try the [[Neural Network#Deep Neural Network (DNN)|Deep Neural Network (DNN)]] e.g. [[Image Classification]]
**** which is a type of is predecessors... [[Feed Forward Neural Network (FF or FFNN)]] and [[Artificial Neural Network (ANN)]]
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**** which is a type of is predecessors... [[Feed Forward Neural Network (FF or FFNN)]] and [[Neural Network]]
  
 
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Revision as of 14:46, 9 May 2023

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Classification problems are sometimes divided into binary (yes or no) and multi-category problems (animal, vegetable, or mineral). Classifiers are ubiquitous in data science. The world around is full of classifiers. Classifiers help in identifying customers who may churn. Classifiers help in predicting whether it will rain or not. Classifiers help in preventing spam e-mails. If the targets are designed to be binary (two-class classification) then a binary classifier is used, the target will only take a 0 or 1 value. Machine learning algorithms explained | Martin Heller - InfoWorld