Difference between revisions of "...predict categories"
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* ...yes, then try the [[Support Vector Machine (SVM)]] for multi-class classification | * ...yes, then try the [[Support Vector Machine (SVM)]] for multi-class classification | ||
* ...yes, then try the [[Naive Bayes]] for multi-class classification | * ...yes, then try the [[Naive Bayes]] for multi-class classification | ||
| + | * ...yes, then try the [[Feed Forward Neural Network (FF or FFNN)]] for multi-class classification | ||
| + | * ...yes, then try the [[Artificial Neural Network (ANN)]] for multi-class classification | ||
| + | * ...yes, then try the [[Deep Neural Network (DNN)]] for multi-class classification | ||
Revision as of 08:06, 3 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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If you ...
- ...have two-class classification, then try the Perceptron (P)
- ...yes, then try the Boosted Decision Tree
- ...yes, then try the Logistic Regression for multi-class classification
- ...yes, then try the Decision Forest for multi-class classification
- ...yes, then try the Decision Jungle for multi-class classification
- ...yes, then try the Support Vector Machine (SVM) for multi-class classification
- ...yes, then try the Naive Bayes for multi-class classification
- ...yes, then try the Feed Forward Neural Network (FF or FFNN) for multi-class classification
- ...yes, then try the Artificial Neural Network (ANN) for multi-class classification
- ...yes, then try the Deep Neural Network (DNN) for multi-class classification
