Overfitting
When a model learns training data too well, including noise, and performs poorly on new data.
In-depth explanation
Overfitting occurs when a model becomes too complex and memorizes the training data rather than learning generalizable patterns. Signs include high training accuracy but low validation accuracy. Prevention techniques include regularization, cross-validation, early stopping, dropout, and using more training data.
Examples
Related terms
More in Machine Learning
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Cross-Validation
A technique to evaluate model performance by training and testing on different subsets of data.
Ensemble Learning
Combining multiple models to produce better predictions than any single model.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
Feature Engineering
The process of using domain knowledge to create new features that improve model performance.
Gradient Descent
An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
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