The correct answer is A. Overfitting.
Overfitting is a common problem in machine learning that occurs when a model learns the training data too well and is unable to generalize to new data. This can happen when the model is too complex or when the training data is not representative of the data that the model will be used on.
To avoid overfitting, it is important to use a model that is not too complex and to use a large enough training set. It is also important to use regularization techniques, such as L2 regularization, which can help to prevent the model from overfitting.
B. Overlearning is not a common term in machine learning. It is possible that the question is referring to overfitting, which is a common problem in machine learning.
C. Classification is a type of machine learning task in which the model is trained to assign labels to data points.
D. Regression is a type of machine learning task in which the model is trained to predict a continuous value for each data point.