How can you tell if a graph is overfitting or Underfitting?
Overfitting is easy to diagnose with the accuracy visualizations you have available. If “Accuracy” (measured against the training set) is very good and “Validation Accuracy” (measured against a validation set) is not as good, then your model is overfitting.
What is the reason to have different train and test accuracies?
There will be some gap between train and validation and test accuracies because of distribution of data. If you wanted to make sure if this is happening because of some set of data, try to run the models by changing the seed with different values.
What does it mean when a training set is overfitting?
During the training phase, even if it is accurately classifying all the data in the training set, if it keeps getting things wrong in the validation set, we can safely assume that it is overfitting to the training set, because that would mean that it is not generalizing well to points it hasn’t encountered.
What is the difference between overfitting and underfitting?
The problem of Overfitting vs Underfitting finally appears when we talk about the polynomial degree. The degree represents how much flexibility is in the model, with a higher power allowing the model freedom to hit as many data points as possible. An underfit model will be less flexible and cannot account for the data.
What should the ratio of training and testing be?
I like to keep a 4:1 ratio, 4/5 of the data dedicated to training the program and 1/5 of the data dedicated to testing the program. The separate testing set should be foreign to the machine learning algorithm and the purpose of it is to test if what it learned can really go out and do useful things accurately (e.g. making predictions on data).
What does high training score and low test score mean?
Usually, high training score and low test score is over-fitting. Very low training score and low test score is under-fitting. First example here, in technical term is called low bias and high variance which is over-fitting. The latter example, high variance and high bias called under-fitting.