Answers.Body: <p>Any classifier that performs slightly better than random guessing is a weak classifier. In the case of a system with N classes, a random guessing actor will have an accuracy of <strong>100/N</strong>.</p>
  <p>For example, if there are 1000 classes, a random guesser will have an accuracy of 0.1%. Hence any classifier with more than 0.1% accuracy is a weak classifier in this example.</p>
  
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Answers.CreationDate: 2022-02-20T21:11:12.977Z
Answers.LastActivityDate: 2022-02-20T21:11:12.977Z
Answers.UserDisplayName: Sooryakiran Pallikulathil
Body: <p>Normally, when using an ensemble method, such as baggin or boosting, in binary classification, there is a reqauirment that each weak classifier have accuracy better than 50%.</p>
  <p>In the multiclass claaification setting, this is often infeasible. Is there a way to improve upon multiclass classification with ensembles.</p>
  <p>For an example to make this concrete:
  Say I have a problem with 1000 classes, and I train 50 models, each with 10% accuracy, which is 100x better than random guessing.</p>
  <p>Is there a way to combine these models to form a better classification algorithm?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-02-17T09:45:31.580Z
Comments.Text: Can you please put your **specific question** in the title? "Multiclass Ensemble Methods" is not a question and it's also not specific.
Comments.UserDisplayName: nbro
Tags: <machine-learning><classification><multiclass-classification><ensemble-learning><boosting>
Title: Multiclass Ensemble Methods with weak classifiers under 50%
UserDisplayName: chessprogrammer
ThreadText: # Multiclass Ensemble Methods with weak classifiers under 50%
  
  (Asked by: username_0 on 2022-02-17)
  
  Normally, when using an ensemble method, such as baggin or boosting, in binary classification, there is a reqauirment that each weak classifier have accuracy better than 50%.
  
  In the multiclass claaification setting, this is often infeasible. Is there a way to improve upon multiclass classification with ensembles.
  
  For an example to make this concrete:
  Say I have a problem with 1000 classes, and I train 50 models, each with 10% accuracy, which is 100x better than random guessing.
  
  Is there a way to combine these models to form a better classification algorithm?
  
  **Tags:** machine-learning, classification, multiclass-classification, ensemble-learning, boosting
  
  > **Comments:**
  > **username_1**: Can you please put your **specific question** in the title? "Multiclass Ensemble Methods" is not a question and it's also not specific.  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2022-02-20. Score: 0
  
  Any classifier that performs slightly better than random guessing is a weak classifier. In the case of a system with N classes, a random guessing actor will have an accuracy of **100/N**.
  
  For example, if there are 1000 classes, a random guesser will have an accuracy of 0.1%. Hence any classifier with more than 0.1% accuracy is a weak classifier in this example.
  
  
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