Hi Julianne,
Perfect separation in an SVM may indicate overfitting as opposed to being a desirable result. When a model learns the training data, which may include noise and outliers, too well, it becomes overfit and is unable to generalize well to unseen data.
Since you have performed k-fold cross-validation and used held-out test data, perfect separation may also be desirable. But if the training data and test data are highly correlated, perfect separation could again point towards overfitting and be deemed undesirable.
It is vital to make sure that the test data is representative of the real-world scenarios the model would encounter,to handle these problems.
Consider the following steps:
- Diverse and unbiased test data: Make sure the test data includes all the scenarios and variations the model is requiredto handle. Avoid introducing biases that may limit the model's generalization ability.
- Increase the dataset: Collect more information,if you can, to make your dataset bigger. More representative samples can be provided by a larger dataset, which can also aid the model in better identifyingunderlying trends.
- External validation: Try to collect data that is independent of the training data and closely representsreal-world scenarios. This can providea more reliable estimate of the model's performance and generalization ability.
Hope this helps.
