MSE Calculator - Mean Squared Error

Your details

The observed (true) values separated by commas. Must have the same count as predicted values.
The model or forecast values separated by commas. Must have the same count as actual values.
MSEExcellent fit
6

Mean Squared Error - the average of squared residuals

RMSE2.4495
SSE30
MAE2.4
MAPE0.1%
R-squared0.97
n (observations)5
0.97 R²
Poor fit<0.5Moderate0.5-0.7Good fit0.7-0.9Excellent0.9+

MSE = 6.0000, RMSE = 2.4495 across 5 observations.

  • MSE of 6.0000 means the average squared deviation between actual and predicted values is 6.0000.
  • RMSE of 2.4495 expresses that error in the same units as your data, making it easier to interpret alongside the scale of your values.
  • R-squared of 0.9700 indicates the model explains 97.0% of the variance in the actual values.
  • MAPE of 10.30% gives the average percentage error relative to actual values, useful when comparing errors across different scales.

Next stepWith an R-squared above 0.9 your model fits the data well. Consider checking for overfitting if this is a training set.

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