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The Science of Conspiracy: The 211th Evolutionary Lens with Bret Weinstein and Heather Heying
DarkHorse Podcast
Avoid Overfitting and Underfitting in Model Creation
The important trend in machine learning is to create models that are predictive of novel data, not just matches the data that has been fitted into the model. Overfitting occurs when the model is too closely fitted to sample data and fails to predict new data, while underfitting happens when the model does not capture the important trend. In a non-statistical context, there is a tendency to force observations to match the model, which is not a perfect fit for overfitting, but rather a situation where every new observation is forced into the model. It is crucial to create models that capture the important trend in the data without overfitting or underfitting.
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