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Results tagged with training
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user 34036
Training is the part of machine learning whereby a model is "trained" on a define portion of a dataset to learn attributes and statistical features of the data. It's counterparts are called Testing and Validation. After training a model is tested and validated on another portion of the dataset.
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Why would a validation set wear out slower than a test set?
On this page of Google's Machine Learning Crash Course, we find the following statement:
"Test sets and validation sets "wear out" with repeated use. That is, the more you use the same data to make …
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Do models without parameters exist?
Isn't it the whole point of training to tune a model's parameters? …
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Why exactly using a test set for model evaluation is a bad idea?
You won't be changing any parameters of the model (because you are not training). … For instance, at the end of this video, Luis says we are breaking what he calls the "Golden rule" (i.e. never use your testing data for training). …