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Random forests are a popular algorithm in many machine learning competitions, because they are fast, they don't have many parameters to tune, yet still produce good predictions. Among their weaknesess is the fact they can easily overfit a noisy dataset.   \subsubsection{Linear SVM}  Support vector machines\cite{Cortes_1995} are discriminative classifiers formally defined by high-dimensional hyperplanes, which are used to distinguish between the classes to which data points belong. The hyperplane defined by an SVM maximizes the margin to the data points used in training, hoping that this leads to a better generalization of the classifier.   SVM can be used to project the original data to a much higher dimensional space (in some cases even infinte-dimensional space), so that they are linearly separable in that space.  \subsection{Model design}