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I am using scikit-learn to build a multiclass classification model. To this extent, I have tried the RandomForestClassifier, KNeighborsClassifier, LogisticRegression, MultinomialNB, and SVC algorithms. I am satisfied with the generated output. However, I do have a question about the default mechanism used by the algorithms for multiclass classification. I read that all scikit-learn classifiers are capable of multiclass classification, but I could not find any information about the default mechanism used by the algorithms.

desertnaut
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Tony P
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    Some classifiers, like KNeighborsClassifier or RandomForestClassifier are capable of multiclass classification by default. Other, like LogisticRegression, which only capable of saying yes or now, adapt to multiclass by OVR. I do not think such thing as a default "mechanism" exists. – Sergey Bushmanov Oct 22 '20 at 21:14

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One-vs-the-rest or One-vs-all is the most commonly used and a fair default strategy for multiclass classification algorithms. For each classifier, the class is fitted against all the other classes. Check here for more information https://scikit-learn.org/stable/modules/generated/sklearn.multiclass.OneVsRestClassifier.html

gideon
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  • How your answer would apply to `KNeighborsClassifier` or `RandomForestClassifier`? – Sergey Bushmanov Oct 22 '20 at 21:10
  • Edited, Thanks! Multiclass in KNN and Random Forest works inherently. – gideon Oct 22 '20 at 21:35
  • Sorry for the stupid question, but what does it mean by "works inherently"? I noticed that the scikit-learn documentation mentioned the same, but did not understand what it means. – Tony P Oct 22 '20 at 21:44
  • It means it supports multiclass natively do not need any extra manipulation or adaptation like OVR – gideon Oct 22 '20 at 22:17