What is the difference between SVM and Decision Trees?

Can you explain the difference between Support Vector Machines and Decision Trees?

Sure! Support Vector Machines (SVM) are supervised learning models that analyze data for classification and regression tasks. They work by finding the hyperplane that best separates different classes in the feature space. On the other hand, Decision Trees are a flowchart-like structure that makes decisions based on asking a series of questions about the features of the data.

What are the advantages of using SVM over Decision Trees?

SVMs are effective in high-dimensional spaces and are robust against overfitting, especially in cases where the number of dimensions exceeds the number of samples. Decision Trees, while easy to interpret, can easily overfit the data if not pruned properly.

Are there any scenarios where Decision Trees might be preferred?

Yes, Decision Trees are preferred when interpretability is crucial, as they provide clear decision paths. They also handle both numerical and categorical data well and can capture non-linear relationships.

Can SVMs and Decision Trees be used together?

Absolutely! They can be combined in ensemble methods like Random Forests or used in a hybrid model to leverage the strengths of both algorithms.