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Support Vector Machine (SVM)

ML Models & Algorithms
An early model often used for classification tasks.

Support Vector Machine (SVM) is a classification algorithm that works by separating data into categories by finding the best “decision boundary” (or hyperplane). SVM models are robust and work well for complex, high-dimensional datasets.
The model supports both linear and non-linear tasks. Common use cases include classifying ad copy sentiment (a type of sentiment-based classification) and audience segmentation. SVM is also listed as an example algorithm for implementing Search Intent Classification and Page Type Identification projects.

Related Concepts

Machine Learning (ML) Multi-Class Classification Supervised Learning Binary Classification Feature Extraction

Sources & References

Introduction to Machine Learning for SEOs Course
academy.mlforseo.com

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