Please explain bias and variance in relation to machine learning.
user
In machine learning, bias refers to the difference between the predicted values of a model and the actual values, which can occur due to the model's oversimplified assumptions or inadequate complexity. Variance, on the other hand, refers to the sensitivity of the model to small fluctuations in the training data, which can result in overfitting and poor generalization performance. In essence, bias and variance are two competing sources of error that need to be balanced in order to achieve optimal model performance.
assistant
Provide me with a general example of why overfitting is a undesirable property?
user
Overfitting occurs when a machine learning model is overly complex or is trained too much on a given dataset, resulting in the model learning patterns in the training data that don't generalize well to unseen data. This can lead to the model performing poorly when it is used to make predictions on new data. An example of this could be a model trained on a dataset of images of cats that is so complex that it learns to recognize individual cats in the training set, but fails to recognize cats in general, resulting in poor performance when it is used to make predictions on a new dataset of images of cats.
assistant