One way to tell if you’re overfitting is to compare the accuracy of the model on the training data to the accuracy of the model on the test data. If the accuracy of the model is significantly higher on the training data than on the test data, then it’s likely that the model is overfitting. Additionally, you can look at the complexity of the model; if the model is overly complex, it may be overfitting. Finally, you can also look at the data; if the data is too closely related or is not varied enough, the model may be overfitting.

How do you know if you are overfitting or Underfitting?

A good way to tell if you are overfitting or underfitting is to look at the performance of your model on the training set and on the validation set. If the performance of the model on the training set is much better than on the validation set, then it is likely that the model is overfitting. If the performance on the training set is much worse than on the validation set, then it is likely that the model is underfitting. Additionally, you can also check the learning curves of the model, which can help you identify if the model is overfitting or underfitting.

How do you prove you’re not overfitting?

Proving that you are not overfitting a model can be done in several ways. One way is to use cross-validation. This involves splitting your dataset into a training set and a validation set, and then training and evaluating the model on the training set. If the model performs well on the training set but not on the validation set, it is likely that the model is overfitting. Another way to check for overfitting is to use a holdout set, which is a subset of the data that is held out from the training and validation sets and used to check the model’s performance on unseen data. Additionally, regularization techniques such as L2 regularization can be used to reduce the risk of overfitting.