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one vs one

Innocent Iguana answered on January 8, 2024 Popularity 10/10 Helpfulness 2/10

answer one vs one

one vs one

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- One-vs-Rest (OvR) or One-vs-All:
    - a two class classifier trained for each class against the rest of the classes.
    -  Classifier with the highest confidence is chosen as the final prediction.
- One-vs-One (OvO):
    - binary classifiers are trained for each pair of combination of classes.
    - For n classes, n-1 classifiers are trained with pairs
    - Final output is determined by majority of votes amongst the classifiers
- Multinomial (Softmax):
    - directly trains a single classifier to predict the probabilities of each class.
    - uses a "softmax" activation function, which converts the raw outputs into class probabilities.
    - Calculates hyperplane distance with features and weights for each class. The distance is then converted to probability.
    - class with the highest probability is chosen as the final prediction.
    - nothing but the generalized argmax of multiple singular logistic regression

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Contributed on Jan 08 2024

Innocent Iguana

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