What are the limitations of using the linear kernel in SVM, and how can other kernels overcome these limitations?
- Can't handle non-linear data
- It's too slow
- Too easy to implement
- Too many parameters
The linear kernel in SVM is limited to handling linearly separable data. Other kernels, like polynomial or RBF, can transform the feature space to handle non-linear data.
Loading...
Related Quiz
- The addition of _________ in the loss function is a common technique to regularize the model and prevent overfitting.
- t-SNE is particularly known for preserving which kind of structures from the high-dimensional data in the low-dimensional representation?
- How does the objective function differ between Ridge, Lasso, and ElasticNet?
- What is the primary function of the hyperparameters in SVM?
- You have a dataset with many correlated features, and you decide to use PCA. How would you determine which Eigenvectors to keep?