Random forest
Many decision trees trained in parallel on different random samples of rows and columns, with their predictions averaged. Hard to overfit, almost free of tuning, and a good first baseline.
A definition is not an understanding. This is taught properly in Learning a mapping from examples, part of Supervised learning.
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