![]() ![]() Therefore it makes perfect sense for us to now transition onto interview questions related to them. The above should have already got anyone excited regarding Support Vector Machines and their importance and use. Support Vector Machines ML Interview Questions & Answers This also results in SVMs being effective in high dimensional spaces, even when the dimensions are higher than the number of samples in the data. This enables SVMs to decipher much more complex relationships between the data points in the data that we present to them without the onus being on us to perform the complicated transformations. One such benefit is that they can be used for not only linear classifications or regressions but also non-linear ones. We need SVMs due to the benefits we get from incorporating them in our ML models. Why do we need to use Support Vector Machines?
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