Nonlinear algorithms

Support Vector Machine (SVM) is a powerful and advanced supervised learning technique for classification and regression that can automatically fit linear and nonlinear models.

SVM algorithms have quite a few advantages against other machine learning algorithms:

  • They can handle the majority of supervised problems such as regression, classification, and anomaly detection (anyway, they are actually best at binary classification).
  • They provide a good handling of noisy data and outliers. They tend to overfit less, since they only work with some particular examples, the support vectors.
  • They work fine with datasets presenting more features than examples, though, as with other machine learning algorithms, SVM would gain both ...

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