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Machine Learning: Training vs Testing by 賴柏妤 is a document available to read on EtoBox.

What is Machine Learning: Training vs Testing about?

The document discusses the concepts of training versus testing in machine learning, focusing on the relationship between the error rates of hypotheses during training and testing phases. It introduces key questions regarding the effectiveness of learning, particularly how to ensure that the error on unseen data is close to the error on training data while minimizing training error. Additionally, it explores the effective number of hypotheses and lines in relation to input data, emphasizing the importance of

Author

賴柏妤

Language

EN