In linear regression models, we can easily improve the fit, as measured by $R^2,$ by including a whole bunch of predictor variables, some of which may have no economic justification and not relevant. We often call modelling of this sort as This occurs when an estimated model begins to describe the quirks of the data rather than the real relationships between variables.

a$.$ distribution

b$.$ oversampling

c$.$ overfitting

d$.$ partitioning