Support vector machines are considered some of the best classifiers in supervised learning for analyzing complex data and downplaying the influence of outliers.
Developed within the computer science community in the 1990s, SVM was originally designed for predicting numeric and categorical outcomes as a double-barrel prediction technique. Today, SVM is mostly used as a classification technique for predicting categorical outcomes, similar to logistic regression.
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