Overfitting
Machine Learning
Data points that come from a different distribution than the training data.
Detailed Explanation
Out-of-Distribution (OOD) Data refers to data that differs significantly from the training dataset's distribution, making it challenging for machine learning models to generalize accurately. Detecting OOD data is crucial for model reliability, as it can cause unpredictable behavior or errors. Techniques for identifying OOD data include uncertainty estimation and specialized algorithms designed to flag unfamiliar inputs.
Use Cases
- •Detect OOD data during deployment to prevent unreliable model predictions and ensure robustness in real-world applications.
Related Terms
Other terms in the Machine Learning category