#evaluation
Explore Books, Authors and Common Highlights on Evaluation
Showing 8 of 8 highlights
Good design is actually a lot harder to notice than bad design.
Evaluation metrics guide the selection of the best model.
From The Hundred-Page Machine Learning Book by Andriy Burkov
A good model is one that generalizes well to unseen data.
From The Hundred-Page Machine Learning Book by Andriy Burkov
Overfitting occurs when a model learns the training data too well, including its noise.
From The Hundred-Page Machine Learning Book by Andriy Burkov
Theories are not simply true or false; they are also more or less useful.
From The Structure of Scientific Revolutions by Thomas S. Kuhn
Overfitting occurs when a model learns the training data too well, including noise and outliers, leading to poor generalization.
From Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Metrics are important, but they are not the only thing that matters.
From The Lean Startup by Eric Ries
The Turing Test is a way to evaluate a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.
From Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig