Overfitting: Difference between revisions
Thakshashila (talk | contribs) Created page with "= Overfitting = '''Overfitting''' is a common problem in machine learning where a model learns the training data too well, including its noise and outliers, resulting in poor performance on new, unseen data. == What is Overfitting? == When a model is overfitted, it captures not only the underlying pattern but also the random fluctuations or noise in the training dataset. This causes the model to perform excellently on training data but badly on test or real-world data..." |
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* [[Underfitting]] | * [[Underfitting]] | ||
* [[Cross | * [[Cross Validation]] | ||
* [[Regularization]] | * [[Regularization]] | ||
* [[Bias-Variance Tradeoff]] | * [[Bias-Variance Tradeoff]] | ||