What type of approach does AutoML typically embrace for model selection?

AutoML, or Automated Machine Learning, typically embraces an iterative experimentation approach for model selection. This means that it systematically tests a range of algorithms and their configurations, continually refining the process based on performance metrics.

The iterative nature of this approach allows AutoML frameworks to adaptively choose features, algorithms, and hyperparameters based on the results of previous experiments. By evaluating models against validation data, it can identify which sets perform best, enabling it to converge on an optimal model much more efficiently than if it were relying on fixed parameters or random selection.

This process is vital for achieving high-performing models in diverse datasets and scenarios, as it enables the exploration of various methodologies and combinations without requiring extensive manual intervention. This adaptability and emphasis on performance through systematic testing are what set iterative experimentation apart as a key hallmark of AutoML's methodology.