Bigger Models Fit Smoother, Not Harder
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YouTubeDataMListic
Bigger Models Fit Smoother, Not Harder
Every machine learning course draws the same curve: grow the model and the error on new data falls, then turns around and climbs, so you find the sweet spot and stop. Keep going anyway, far past the peak, and the error comes back down, often lower than the sweet spot ever was. The peak sits exactly where the model has as many knobs as you have ...
7.2K views1 month ago
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If you are beginning to learn machine learning, the number of model names can make the field seem much more complicated than it actually is, but most models can be organized into a few major families based on how they learn and the kinds of problems they are designed to solve. Linear and logistic regression learn relatively simple relationships between variables, while decision trees, random forests, and gradient-boosting models make predictions through combinations of decision rules. K-nearest
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