Difference between revisions of "...predict categories"
| Line 15: | Line 15: | ||
* ...yes, then try the [[Decision Jungle]] 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 [[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 | ||
Revision as of 22:00, 2 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.
___________________________________________________
If you ...
- ...have two-class classification, then try the Averaged Perceptron
- ...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
