Statistics

What is the difference between overfitting and underfitting?

Most people think that model fitting is a machine-learning concept, but it is not true. Model fitting is an old statistical concept widely used in machine learning. To create models, we divide the data set into two parts: the train dataset and the test dataset. The test dataset is also called new data. So, don't get confused by the terms. We create a model on the train data set and test the model on the test data set.

The main difference between overfitting and underfitting is that overfitting has a high-accuracy model on the training data set. Still, it does not perform well on the test data set. Underfitting has a low-accuracy model, so it won't perform well on the test data set.

Which would you select, a linear regression model, R-Square, or adjusted R-square?

R-square and adjusted R-square are two model accuracy measures for linear regression. The R-square will always increase if we add more independent variables to the model. However, adjusted R-square will only increase if the newly added variable improves the model's accuracy. ...