Difference between revisions of "...predict categories"

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** fast training, accurate, then try the [[Random Forest (or) Random Decision Forest]]
 
** fast training, accurate, then try the [[Random Forest (or) Random Decision Forest]]
 
** accurate, then try the [[Decision Jungle]] for multi-class classification
 
** accurate, then try the [[Decision Jungle]] for multi-class classification
** ... accurate, can allow long training times...
+
** accurate, can allow long training times, then try the [[Deep Neural Network (DNN)]]
*** ...then try the [[Deep Neural Network (DNN)]]
 
 
*** which is a type of is predecessors... [[Feed Forward Neural Network (FF or FFNN)]] and [[Artificial Neural Network (ANN)]]
 
*** which is a type of is predecessors... [[Feed Forward Neural Network (FF or FFNN)]] and [[Artificial Neural Network (ANN)]]

Revision as of 19:37, 4 June 2018

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.

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