Generalization error, also known as out-of-sample error or risk, is a measure of how accurately an algorithm can predict outcomes for new data. It is important to minimize generalization error by avoiding overfitting in the learning algorithm. Learning curves are used to visualize the performance of a machine learning algorithm by showing estimates of the generalization error throughout the learning process.
Carnegie Mellon University
Spring 2018
A comprehensive exploration of machine learning theories and practical algorithms. Covers a broad spectrum of topics like decision tree learning, neural networks, statistical learning, and reinforcement learning. Encourages hands-on learning via programming assignments.
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