MSE (Mean Squared Error)
2024 | AI Dictionary
What is MSE: A common metric used to measure model accuracy in regression problems by calculating the average squared difference between predictions and actuals.
What is MSE (Mean Squared Error)?
MSE (Mean Squared Error) is a common metric used to measure the accuracy of a model , especially in regression problems. It calculates the average squared difference between the predicted and actual values, with larger errors being penalized more.
Applications of MSE
- Regression Models: Helps to evaluate the performance of models like linear regression and neural networks.
- Model Tuning: Used to assess the effectiveness of different model parameters or architectures.
- Forecasting: Measures prediction accuracy in time series analysis.
Example of MSE
In predicting house prices, if the predicted price is $300,000 and the actual price is $350,000, the squared error for that prediction would be \((350,000 - 300,000)^2 = 2,500,000,000\). The average of these squared errors across all predictions gives the MSE.
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