What MSE (Mean Squared Error) Meaning, Applications & Example
Mean Squared Error, common loss function for regression.
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.