I have created multiple regression models and wanted to choose the best one. One common metric would be RMSE, as you know.
When I looked at the results, second model (RMSE = 0.15) was better able to detect some of the peaks than the first one (RMSE = 0.1). For example look at the two results:
Here is the second model's result(RMSE = 0.15): The model which is able to detect all the peaks, as much as possible (although its RMSE is not the least among all of the models), is more preferable than a model which has less RMSE but is not able to detect peaks. I searched through the web but didn't find what I was looking for.
Can anyone suggest me a code to evaluate the results of the models, based on their ability to detect peaks better?
Suppose the results are simply 2 arrays:
import numpy as np np.random.seed(10) predicted_1 = np.random.rand(10, 1) predicted_2 = np.random.rand(10, 1) original = np.random.rand(10, 1)