How to reduce the RMSE of a model?
@Manoj Dhake , it depends on the dependent variable. The unit of RMSE is same as dependent variable. If your data has a range of 0 to 100000 then RMSE value of 3000 is small, but if the range goes from 0 to 1, it is pretty huge. Try to play with other input variables, and compare your RMSE values. The smaller the RMSE value, the better the model.
What is wrong with the custom RMSE function?
Grad and hess are the same as in lightgbm source or as given in the answer to following question. What is wrong with the custom RMSE function? Remark: In this example the final loss seems to be close but the trajectory is totally different.
Is the RMSE value the same as dependent variable?
The unit of RMSE is same as dependent variable. If your data has a range of 0 to 100000 then RMSE value of 3000 is small, but if the range goes from 0 to 1, it is pretty huge. Try to play with other input variables, and compare your RMSE values. The smaller the RMSE value, the better the model.
Is there a custom RMSE function for LightGBM?
To start with custom objective functions for lightgbm I started to reproduce standard objective RMSE. Unfortunately, the scores are different. My the example is based on this post or github.
What does RMSE really mean in data science?
If the noise is small, as estimated by RMSE, this generally means our model is good at predicting our observed data, and if RMSE is large, this generally means our model is failing to account for important features underlying our data. In data science, RMSE has a double purpose: To evaluate trained models for usefulness / accuracy
How to know if RMSE is good or bad?
RMSE has the same unit than the predicted values, so, it has to be understood that we have to take a look at the importance of the RMSE in comparaison with the predicted values. How we could know if it is good or not? we have to compute the Scatter Index, which is simply the RMSE divided by the average value of the observed value.