In linear regression, overfitting takes place when the model is very complex. Usually, this situation arises when there are more parameters than the number of observations. A linear regression model with overfitting will not perfectly generalise to new data. So, it will perform efficiently on training data but poorly on test data. Factors responsible for overfitting are –(i) outliers in the train data and (ii) train and test data belonging to different distributions.