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This lecture discusses the concept of data splits and models in machine learning. The instructor explains how to choose the best algorithm for a given task, and how to prevent overfitting with regularization.
This lecture covers how to choose the best model for a given data set using k-fold cross-validation. Student Michael Nielsen explains how to add features to a linear classifier and how to iteratively improve performance by adding features until they no longer improve performance. He also presents a method for feature selection called forward search.
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