Mechanistic Interpretability
Machine Learning
The average of the squared differences between predicted and actual values. Emphasizes larger errors due to squaring.
Detailed Explanation
Mean Squared Error (MSE) is a metric used in machine learning to measure the average squared difference between predicted values and actual outcomes. It penalizes larger errors more heavily because of the squaring process, making it sensitive to significant deviations. MSE is commonly used to evaluate the accuracy of regression models, with lower values indicating better performance.
Use Cases
- •Optimizing a regression model by minimizing MSE to improve prediction accuracy on continuous data.
Related Terms
Other terms in the Machine Learning category