<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>

CC BY-SA 4.0
2022-02-20T21:11:12.977Z
2022-02-20T21:11:12.977Z
Sooryakiran Pallikulathil
<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>

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2022-02-17T09:45:31.580Z
Can you please put your **specific question** in the title? "Multiclass Ensemble Methods" is not a question and it's also not specific.
nbro
<machine-learning><classification><multiclass-classification><ensemble-learning><boosting>
Multiclass Ensemble Methods with weak classifiers under 50%
chessprogrammer
# 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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