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What are the scenarios which have lower training accuracy as well as low test accuracy termed?
What are scenarios which have lower low training accuracy as well as low test accuracy termed as? 1. High Bias.
What should you do if you accuracy is low?
Now we’ll check out the proven way to improve the accuracy of a model:
- Add more data. Having more data is always a good idea.
- Treat missing and Outlier values.
- Feature Engineering.
- Feature Selection.
- Multiple algorithms.
- Algorithm Tuning.
- Ensemble methods.
When can validation accuracy be greater than training accuracy?
If you are using data augmentation to “noisify” your training data, then it can make sense that you are getting better accuracy on the validation set, because it will be an easier dataset. If this is the case, then you don’t really have a problem. As a rule, your validation set should be as close as possible to your test set or real-life use case.
Which is better, validation data or training data?
We’re getting rather odd results, where our validation data is getting better accuracy and lower loss, than our training data. And this is consistent across different sizes of hidden layers. This is our model: And this is an example of the accuracy and losses: and .
When does validation become part of the training set?
Once you tweak your model, or choose among multiple competing models based on the validation set, it is not a true validation set any more – it effectively becomes part of the training set, albeit in a somewhat obfuscated way.
When is validation set too small for machine learning?
If the validation set is to small it does not adequately represent the probability distribution of the data. If your training set is small there is not enough data to adequately train the model. Also your model is very basic and may not be adequate to cover the complexity of the data.