The MSE value from my ANN training is over a hundred for water quality forecasting. Is that mean that my ANN model is bad because the larger MSE means the larger the error.
for example, the R-value is 0.98962, 0.72513; MSE is 19135.5455, 93641.6775 during training and testing respectively.
The mean squared error (MSE) is a measure of the difference between the predicted values and the actual values. A larger MSE value generally indicates a larger difference between the predicted values and the actual values, which means that the model is less accurate. In your case, the MSE value of over a hundred for water quality forecasting suggests that the model is not performing well.
However, it is important to keep in mind that the MSE value alone is not always a reliable indicator of the performance of a model. The R-value, which is the coefficient of determination, can also give an idea of the model's performance. A higher R-value indicates a stronger correlation between the predicted values and the actual values and generally indicates a better model fit. In your case, the R-value is 0.98962 and 0.72513 during training and testing, respectively.
So, a high R-value but a high MSE could indicate that the model is overfitting, it is good at predicting the training data but not generalizing to new data. In this case, you may want to try different techniques to reduce overfitting such as regularization, early stopping, and cross-validation.
It is also important to consider the context of the problem and the specific requirements of the application. For example, a low MSE might be acceptable for certain types of problems, such as weather forecasting, where small errors might not be significant. In other cases, such as water quality forecasting, even small errors can have serious consequences, so a low MSE is essential.
In summary, the MSE value is an important indicator of the model's performance but it should be considered along with other performance metrics such as R-value, and the specific requirements of the application.
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