Mean Squared Error (MSE)

1. 1Define a problem to forecast using a machine learning model
2. 2Calculate the predicted values using the model
3. 3Compute the Mean Squared Error (MSE) between predicted and actual values
4. 4Compare the MSE with a baseline model to evaluate performance
5. 5Use the MSE to tune hyperparameters and improve the model

Data scientists and machine learning engineers can benefit from understanding MSE to evaluate and improve their models

💡 MSE is a key metric to evaluate the performance of a forecast model

💡 Evaluate your forecasts with Mean Squared Error (MSE) to measure accuracy