What is RMSE in neural network?
Root mean square error or root mean square deviation is one of the most commonly used measures for evaluating the quality of predictions. RMSE is commonly used in supervised learning applications, as RMSE uses and needs true measurements at each predicted data point.
Is RMSE scale dependent?
RMSE is a good measure of accuracy, but only to compare prediction errors of different models or model configurations for a particular variable and not between variables, as it is scale-dependent.
Can RMSE be used for classification?
Mean square error can certainly be (and is) calculated for forecasts or predicted values of continuous variables, but I think not for classifications. Technically you can, but the MSE function is non-convex for binary classification.
How much mean square error is good?
There are no acceptable limits for MSE except that the lower the MSE the higher the accuracy of prediction as there would be excellent match between the actual and predicted data set. This is as exemplified by improvement in correlation as MSE approaches zero.
When to use min max scaling in neural networks?
For Neural Networks, works best in the range 0-1. Min-Max scaling (or Normalization) is the approach to follow. Now on the outliers, in most scenarios we have to clip those, as outliers are not common, you don’t want outliers to affect your model (unless Anomaly detection is the problem that you are solving).
Can You normalize the RMSE of two models?
Keep in mind that you can always normalize the RMSE. For the second question, i.e., about comparing two models with different datasets by using RMSE, you may do that provided that the DV is the same in both models. Here, the smaller the better but remember that small differences between those RMSE may not be relevant or even significant.
How to normalize data before training a neural network?
All the variables have roughly normal distributions. I consider different options to scale the data before training. One option is to scale the input (independent) and output (dependent) variables to [0, 1] by computing cumulative distribution function using the mean and standard deviation values of each variable, independently.
Is there such a thing as a good RMSE?
First of all, as the earlier commenter R. Astur explains, there is no such thing as a good RMSE, because it is scale-dependent, i.e. dependent on your dependent variable. Hence one can not claim a universal number as a good RMSE. Even if you go for scale-free measures of fit such as MAPE or MASE, you still can not claim a threshold of being good.