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
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Regression
Predicting a continuous numerical value based on input features.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
Master Overfitting.
Learn how to apply this concept with hands-on projects in our comprehensive AI programs.