Difference between revisions of "...predict categories"

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If you ...
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Do you have...
 
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* ...two-class classification; two predicting categories?
* ...have two-class classification; two predicting categories...
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** Do you need the results to be explainable?
** fast training, linear, then try the [[Perceptron (P)]]  
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*** Yes
** fast training, linear, and the features are independent, then try the two-class [[Naive Bayes]] point machine
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**** fast training, accurate, and can have a large footprint, then try the [[(Boosted) Decision Tree]]  
** linear, greater than 100 features, then try the [[Support Vector Machine (SVM)]]
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**** linear, greater than 100 features, then try the [[Support Vector Machine (SVM)]]
** fast training, accurate, and can have a large footprint, then try the [[(Boosted) Decision Tree]]  
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*** No
* ...have multi-class classification; three or more categories...
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**** fast training, linear, then try the [[Perceptron (P)]]  
** linear, then try the [[Naive Bayes]] 
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**** fast training, linear, and the features are independent, then try the two-class [[Naive Bayes]] point machine
** fast training, linear, then try the [[Logistic Regression (LR)]]   
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* ...multi-class classification; three or more categories?
** fast training, accurate, then try the [[Random Forest (or) Random Decision Forest]]
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** Do you need the results to be explainable?
** accurate, then try the [[Decision Jungle]] for multi-class classification
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*** Yes
** accurate, can allow long training times, then try the [[Deep Neural Network (DNN)]]
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**** fast training, linear, then try the [[Logistic Regression (LR)]]   
*** which is a type of is predecessors... [[Feed Forward Neural Network (FF or FFNN)]] and [[Artificial Neural Network (ANN)]]
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**** accurate, then try the [[Decision Jungle]] for multi-class classification
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**** fast training, accurate, then try the [[Random Forest (or) Random Decision Forest]]
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*** No
 +
**** linear, then try the [[Naive Bayes]]
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**** accurate, can allow long training times, then try the [[Deep Neural Network (DNN)]]
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**** which is a type of is predecessors... [[Feed Forward Neural Network (FF or FFNN)]] and [[Artificial Neural Network (ANN)]]

Revision as of 14:27, 7 January 2019

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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Do you have...