What is a good accuracy for neural network?
If you are working on a classification problem, the best score is 100% accuracy. If you are working on a regression problem, the best score is 0.0 error.
Is Overfitting 100 accurate?
3 Answers. Nope, you shouldnot get 100% accuracy from your training dataset. If it does, it could mean that your model is overfitting. The most important question in classification (supervised learning) is that of generalization, that is to say the performances in production (or on the testing dataset).
When to use unstable accuracy in neural network?
With some sets I’m getting very unstable accuracy – Can jump from 70% to 90% and back to 70% and back and fourth. My question is: Let’s say I hit 90% accuracy after 40 iterations (~8 epochs) – Does this mean that the net had reached an optimal state or could it be that it just had a lucky guess on the validation set?
How to identify if your neural network is overfitting?
How to identify if your model is overfitting? you can just cross check the training accuracy and testing accuracy. If training accuracy is much higher than testing accuracy then you can posit that your model has overfitted. You can also plot the predicted points on a graph to verify. There are some techniques to avoid overfitting:
How are neural networks used in data science?
Neural networks are machine learning algorithms that provide state of the accuracy on many use cases. But, a lot of times the accuracy of the network we are building might not be satisfactory or might not take us to the top positions on the leaderboard in data science competitions.
What’s the accuracy of a machine learning network?
Considering your training accuracy can reach >.99, your network seems have enough connections to fully model your data, but you may have extraneous connections that are learning randomly (i.e. overfitting).