Support Vector Machine, Machine Learning, Data Science, Deep Learning.
learned. Drawbacks of using SVMs include: Overfitting: SVMs can overfit the training data if the kernel function and regularization parameters are not chosen carefully. Sensitivity to scaling: SVMs are sensitive to the scaling of the input features, so it is important to scale the features before