What are the best classification algorithms for small datasets?

What are the best classification algorithms for small datasets?

Some of the best classification algorithms for small datasets include Decision Trees, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM). They tend to perform well even with limited data.

Can you explain why Decision Trees are effective for small datasets?

Decision Trees are effective because they can capture complex relationships in the data without requiring a large amount of training data. They also provide clear interpretability.

What about k-NN? How does it work?

k-NN works by finding the 'k' nearest data points to a new instance and classifying it based on the majority class among those neighbors. It's simple and effective for small datasets.

Are there any drawbacks to using these algorithms?

Yes, Decision Trees can overfit if not pruned properly, and k-NN can be sensitive to irrelevant features and the choice of 'k'.