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#evaluation

Explore Books, Authors and Common Highlights on Evaluation

Showing 8 of 8 highlights

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

#evaluation#intelligence#turing-test#humans
Theories are not simply true or false; they are also more or less useful.

From The Structure of Scientific Revolutions by Thomas S. Kuhn

#theory#usefulness#science#evaluation
Good design is actually a lot harder to notice than bad design.

From The Design of Everyday Things by Don Norman

#design#perception#evaluation
Overfitting occurs when a model learns the training data too well, including its noise.

From The Hundred-Page Machine Learning Book by Andriy Burkov

#modeling#overfitting#data#evaluation
A good model is one that generalizes well to unseen data.

From The Hundred-Page Machine Learning Book by Andriy Burkov

#modeling#generalization#data#evaluation
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

#statistics#overfitting#generalization#model-evaluation
Evaluation metrics guide the selection of the best model.

From The Hundred-Page Machine Learning Book by Andriy Burkov

#evaluation#metrics#model-selection#guidance
Metrics are important, but they are not the only thing that matters.

From The Lean Startup by Eric Ries

#metrics#evaluation#business