I trained the model on my custom dataset in which each line contains 3 events (e1, e2, e3) (a tri-gram) where I want to predict e3 based on e1 and e2. An example is shown below:
After training for 50 epochs I got:
On the predict stage, I kept num_candidates=1, then I got very low F1-score
num_classes in my dataset are 9. If I increase, num_candidates, divide by zero error occurred. What is the best approach to choose num_candidates for given dataset?
If I try to address this increasing candidate size will increase accuracy and will be less sensitive to anomalies vise versa. threshold here is window size.
I trained the model on my custom dataset in which each line contains 3 events (e1, e2, e3) (a tri-gram) where I want to predict e3 based on e1 and e2. An example is shown below:
After training for 50 epochs I got:
![image](https://user-images.githubusercontent.com/30209002/175777315-191fec8c-37a2-4a7b-aead-dec71c02cc99.png)
On the predict stage, I kept![image](https://user-images.githubusercontent.com/30209002/175777482-40f9a4ca-1349-465d-bee0-a82a76e10afe.png)
num_candidates=1
, then I got very low F1-scorenum_classes
in my dataset are 9. If I increase, num_candidates, divide by zero error occurred. What is the best approach to choose num_candidates for given dataset?Thanks