Evaluation Metrics, Overfitting/Underfitting
A model has very low error on training data but very high error on validation data. This is a clear sign of:
Which of the following is NOT an effective way to reduce overfitting?
In the bias-variance tradeoff, underfitting is mainly caused by:
What is the main purpose of a validation set during training?
Which technique directly reduces model variance without significantly increasing bias?
A model performs poorly on the training set itself. The best first step to improve it is: