Overfitting and underfitting are the two biggest causes of poor generalization in machine learning algorithms. The data used to build a model is ge...

Overfitting and underfitting are the two biggest causes of poor generalization in machine learning algorithms. The data used to build a model is generally divided into three sets: training, validation, and testing. When there is an imbalance in this adjustment, one of these events may occur. Considering the above, when does overfitting occur?


A. When a predictive model is accurate but takes a long time to execute;
B. When the model learns details of the training data that cannot be generalized to a larger dataset;
C. When hyperparameter tuning is performed and performance is increased.