Hey, have you ever encountered a situation where your ANN model is giving you 99.4% accuracy when you round off the output, but a whopping 0% accuracy when you don’t? Yeah, it’s weird. I’ve been there too.
At first, I thought I was doing something wrong. Maybe there was a bug in my code or my data was messed up. But after digging deeper, I realized that this phenomenon is more common than I thought.
So, what’s going on here? Is it a problem with our models or our approach? Should we be worried about this kind of behavior?
In my opinion, this issue highlights the importance of understanding our models and their limitations. Rounding off the output might give us a false sense of security, making us think our models are performing better than they actually are.
On the other hand, getting 0% accuracy without rounding off might indicate that our models are not generalizing well or are overfitting to the training data.
Either way, it’s crucial to investigate and understand the reasons behind this behavior. Maybe it’s time to revisit our data preprocessing, model architecture, or training procedures.
What do you think? Have you encountered similar issues with your ANN models? Share your experiences and insights!