it' always good idea to try ensemble learning out it' been proven time and time again that it will produce better results than any single modelso definitely consider itsupport vector machine overview finallywe're going to talk about support vector machines (svm)which is very advanced way of clustering or classifying higher dimensional data sowhat if you have multiple features that you want to predict fromsvm can be very powerful tool for doing thatand the results can be scarily goodit' very complicated under the hoodbut the important things are understanding when to use itand how it works at higher level solet' cover svm now support vector machines is fancy name for what actually is fancy concept but fortunatelyit' pretty easy to use the important thing is knowing what it doesand what it' good for sosupport vector machines works well for classifying higher-dimensional dataand by that mean lots of different features soit' easy to use something like kmeans clusteringto cluster data that has two dimensionsyou knowmaybe age on one axis and income on another butwhat if have manymany different features that ' trying to predict from wellsupport vector machines might be good way of doing that support vector machines finds higher-dimensional support vectors across which to divide the data (mathematicallythese support vectors define hyperplanesthat ismathematicallywhat support vector machines can do is find higher dimensional support vectors (that' where it gets its name fromthat define the higher-dimensional planes that split the data into different clusters obviously the math gets pretty weird pretty quickly with all this fortunatelythe tdjljumfbso package will do it all for youwithout you having to actually get into it under the hoodyou need to understand though that it uses something called the kernel trick to actually find those support vectors or hyperplanes that might not be apparent in lower dimensions there are different kernels you can useto do this in different ways the main point is that svm' are good choice if you have higherdimensional data with lots of different featuresand there are different kernels you can use that have varying computational costs and might be better fits for the problem at hand the important point is that svms employ some advanced mathematical trickery to cluster dataand it can handle data sets with lots of features it' also fairly expensive the "kernel trickis the only thing that makes it possible