prompter: what is the difference between under-fitting and over-fitting

assistant: Underfitting and overfitting are problems that occur during the model training process.

Underfitting occurs when the model is too simple and cannot accurately capture the pattern in the training data. As a result, the model has poor performance on both the training data and new, unseen data. Underfitting is often caused by not having enough features or a lack of model complexity.

Overfitting occurs when a model has too many parameters relative to the number of observations in the training data, and it starts to memorize the training data instead of learning the underlying patterns. This leads to high training accuracy but poor generalization performance on unseen data

To prevent underfitting and overfitting, a balance needs to be struck between model complexity and the amount of training data. Techniques such as cross-validation, regularization, and early stopping can also help to prevent overfitting.

prompter: Which metric is most important to look at to detect if over-fitting is taking place?

assistant: One of the metrics that can be used to detect if a machine learning model is overfitting is looking at the loss function while training. A very small loss function usually indicates that overfitting is taking place and can be used to determine when to stop the training process.

assistant: The metric to focus on depends on the data mining goal you are trying to achieve. It is important to use cross-validation to detect if over-fitting is taking place. Cross-validation takes a dataset and splits it randomly (or using a stratified sampling) into multiple validation datasets. Then the models creates its predictions using these validation sets to calculate the metrics for each dataset. If overfitting takes place, the metrics of your model between these validation datasets will differ substantially. If it does not take place, the metrics between the validation datasets should be similar.

assistant: When introduced to never before seen datapoints, an overfitted model will perform very poorly, despite reporting high accuracy during its training. If this trend can be observed, it's safe to assume overfitting has taken place.
It's highly dependent on the structure, but it's generally seen as prudent to have a linear relationship between the number of datapoints and degrees of freedom.

assistant: Which metric is best depends on the kind of task, e.g. whether it is a (binary) classification or generative task.
 
For a (binary) classification task a good choice is the $F_1$-Score. It is calculated by $\frac{2}{\frac{1}{\text{precision}}+\frac{1}{\text{recall}}}$, where recall is a measure for how well did the model find the positives and precision is a measure for how many positive predictions where correct. So a high recall would mean that many positives were correctly classified and a high precision would mean that only few of the negatives were classified as positives.

There are many more metrics like AUC (Area Under Curve), MSE (Mean SquaredError) and Categorical Crossentropy and you will have to try out what works best for you and your specific task.

assistant: Under-fitting in an AI model is when the model is unable to produce a good output because the problem is more complex than the model is capable of solving, this can be caused by many factors some of them may be: The model may not have enough training time, this can be solved without much effort by just further training the model, another cause for under-fitting may be that the training data is simply too complex for the model to solve and the number of parameters may need to be increased.

Over-fitting is the opposite problem to under-fitting where the model may have too many parameters compared to the amount of data, this can cause the model to memorize the solution to the training data instead of learning how to find the solution, this problem can be solved by increasing the amount of training data or decreasing the number of parameters.

prompter: Can you provide a more specific example of how model over-fitting and under-fitting?

assistant: Assuming you are referring to data science, overfitting is the opposite of underfitting, occurring when the model has been overtrained or when it contains too much complexity, resulting in high error rates on test data. Overfitting a model is more common than underfitting one, and underfitting typically occurs in an effort to avoid overfitting through a process called “early stopping.”

prompter: How can you tell when a model is overfit?

assistant: You would be able to tell when a model is overfit when it is has 'memorized' the training data. A symptom of an overfit model is if it performs great on the training data but cannot perform well on data that it has never seen before.

assistant: A model is overfit it fits too closely to a training set of data, this resuts in the model not being abe to preform its job well on new images. If your mode has low error rates, and high variance, these are good signals of overfitting a model. To prevent this you should set aside a portion of testing images that are differnt from the rest to "test" for overfitting. If the images for training have a lower error rate, and the test images have a high error rate then your model is most likely overfitted. This means you might need to tailor your training!

If you have any questions do not be afraid to ask!