Following are the key points described later in this article:
In case there are large number of features and comparatively smaller number of training examples, one would want to use linear kernel. As a matter of fact, it can also be called as SVM with No Kernel. One may recall that SVM with no kernel acts pretty much like logistic regression model where following holds true:
Simply speaking, one may want to use SVM with linear kernel when data distribution is linearly separable.
In scenarios, where there are smaller number of features and large number of training examples, one may use what is called Gaussian Kernel. When working with Gaussian kernel, one may need to choose the value of variance (sigma square). The selection of variance would determine the bias-variance trade-offs. Higher value of variance would result in High bias, low variance classifier and, lower value of variance would result in low bias/high variance classifier.
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