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 over 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).
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.
Work fine with datasets presenting more features than examples, though, as other machine learning algorithms, also SVM would gain both from dimensionality reduction and feature selection.
As drawbacks, we have to mention these:
They provide just estimates, but no probabilities unless you run some time-consuming and computationally intensive probability calibration by means of Platt scaling
They scale super...