Data Science Stack Exchange is a question and answer site for Data science professionals, Machine Learning specialists, and those interested in learning more about the field. It only takes a minute to sign up.
Sign up to join this community
I am using Weka Time Series Forecasting to forecast the trend of the topic NLP in 2018. For that I used evaluate on training option in Weka to obtain Root Mean Squared Error (RMSE). The RMSE value I obtain is 0.785.
0.2) that we can consider to
accept/reject this prediction?
The RMSE is the square root of the variance of the residuals. It indicates the absolute fit of the model to the data–how close the observed data points are to the model’s predicted values. RMSE is an absolute measure of fit. As the square root of a variance, RMSE can be interpreted as the standard deviation of the unexplained variance, and has the useful property of being in the same units as the response variable. Lower values of RMSE indicate better fit.
RMSE is a good measure of how accurately the model predicts the response, and is the most important criterion for fit if the main purpose of the model is prediction.
The best measure of model fit depends on the researcher’s objectives, and more than one are often useful.
The RMSE for your training and your test sets should be very similar if you have built a good model. If the RMSE for the test set is much higher than that of the training set, it is likely that you are badly overffiting the data.
