If you have categorical inputs, you may need to covert them to binary dummy variables (one variable for each category). Numerical Inputs: SVM assumes that your inputs are numeric.
It doesn’t perform very well when the dataset has more noise, i.e.SVMs do not directly provide probability estimates-these are calculated using an expensive five-fold cross-validation process (see scores and probabilities, below).The same set of parameters will not work optimally for all use cases. The main disadvantage of SVM is that it has several key parameters like C, kernel function, and Gamma that all need to be set correctly to achieve the best classification results for any given problem.It works really well with a clear margin of separation.Common kernels are provided, but it’s also possible to specify custom kernels. SVM is versatile: different kernel functions can be specified for the decision function.It’s still effective in cases where the number of dimensions is greater than the number of samples.It’s very effective in high-dimensional spaces as compared to algorithms such as k-nearest neighbors.However, by using a nonlinear kernel as mentioned in the scikit-learn library, we can get a nonlinear classifier without transforming the data or doing heavy computations at all. Normally, the kernel is linear, and we get a linear classifier. Different kernel functions applied on Iris-Dataset
0 kommentar(er)
