Automated algorithm selection

Once you are done with feature processing, you need to find a suitable set of algorithms for training and evaluation.

Every ML algorithm has an ability to solve certain problems. Let's consider clustering algorithms such as k-means, hierarchical clustering, spectral clustering, and DBSCAN. We are familiar with k-means, but what about the others? Each of these algorithms has application areas and each might perform better than others based on the distributional properties of a dataset.

AutoML pipelines can help you to choose the right algorithm from a set of suitable algorithms for a given problem.