Difference between revisions of "Multiclassifiers; Ensembles and Hybrids; Bagging, Boosting, and Stacking"

From
Jump to: navigation, search
Line 1: Line 1:
−
[http://www.youtube.com/results?search_query=boosted+boosting+artificial+intelligence YouTube search...]
+
[http://www.youtube.com/results?search_query=multiclassifiers+ensembles+hybrids+bagging+boosted+boosting+Stacking+artificial+intelligence YouTube search...]
  
 
* [[Boosted Decision Tree]]
 
* [[Boosted Decision Tree]]
 
* [[Boosted Decision Tree Regression]]
 
* [[Boosted Decision Tree Regression]]
−
* [http://xgboost.readthedocs.io/en/latest/model.html Introduction to Boosted Trees | XGBoost]  
+
* [http://www.kdnuggets.com/2017/11/difference-bagging-boosting.html | Xristica, Quantdare @ KDnuggets]
 +
________________________________________________
  
−
________________________________________________
+
* Multiclassifiers - a set of hundreds or thousands of learners with a common objective are fused together to solve the problem. Ensemble and Hybrid methods are a subclasses of multiclassifiers.
 +
 
 +
** Ensemble methods - train multiple models using the same learning algorithm
 +
 
 +
*** Bagging -
 +
 
 +
*** Boosting -
  
−
* Multiclassifiers - a set of hundreds or thousands of learners with a common objective are fused together to solve the problem. Ensemble methods are a subclass of multiclassifiers.
+
** Hybrid Methods -
  
−
* Ensemble methods - train multiple models using the same learning algorithm
+
*** Stacking -  
  
−
A boosted decision tree is an ensemble learning method in which the second tree corrects for the errors of the first tree, the third tree corrects for the errors of the first and second trees, and so forth. Predictions are based on the entire ensemble of trees together that makes the prediction. For further technical details, see the Research section of this article. Generally, when properly configured, boosted decision trees are the easiest methods with which to get top performance on a wide variety of machine learning tasks. However, they are also one of the more memory-intensive learners, and the current implementation holds everything in memory. Therefore, a boosted decision tree model might not be able to process the very large datasets that some linear learners can handle.
 
  
−
http://raw.githubusercontent.com/dmlc/web-data/master/xgboost/model/cart.png
+
https://quantdare.com/wp-content/uploads/2016/04/bb1.png
 +
https://quantdare.com/wp-content/uploads/2016/04/bb2.png
 +
https://quantdare.com/wp-content/uploads/2016/04/bb3.png
 +
https://quantdare.com/wp-content/uploads/2016/04/bb4.png
 +
https://quantdare.com/wp-content/uploads/2016/04/bb5.png
  
 
<youtube>AiePAlZy_t8</youtube>
 
<youtube>AiePAlZy_t8</youtube>

Revision as of 07:56, 4 June 2018

YouTube search...

________________________________________________

  • Multiclassifiers - a set of hundreds or thousands of learners with a common objective are fused together to solve the problem. Ensemble and Hybrid methods are a subclasses of multiclassifiers.
    • Ensemble methods - train multiple models using the same learning algorithm
      • Bagging -
      • Boosting -
    • Hybrid Methods -
      • Stacking -


bb1.png bb2.png bb3.png bb4.png bb5.png

Retrieved from "https://primo.ai/index.php?title=Multiclassifiers;_Ensembles_and_Hybrids;_Bagging,_Boosting,_and_Stacking&oldid=2071"