I am reading the book "An introduction to statistical learning with applications in R". I am reading Logistic Regression and I don't understand why when it's compared to linear regression model, the author said that "the problem with this approach: for balances close to zero we predict a negative probability of default; if we were to predict for very large balances, we would get values bigger than 1." as in the highlight. Could anyone please explain me how the probability can be smaller than 0 or larger than 1?

Your help is really appreciated!

Thank you enter image description here


1 Answer 1


You should just keep reading:

These predictions are not sensible, since of course the true probability of default, regardless of credit card balance, must fall between 0 and 1.

A probability cannot be smaller than 0 or larger than 1. The problem is that the model might predict such a "probability".

  • $\begingroup$ Thanks, but according to the left figure above, the line goes under 0.0 on the y-axis. I wonder what does it mean? $\endgroup$ Mar 14, 2018 at 14:18
  • $\begingroup$ It means that the predicted probability is negative, which is of course meaningless since probabilities cannot be negative. This highlights the limits of logistic regression as a method to predict probabilities. Pragmatically, the results should be "clipped": negative probabilities should be clipped to 0, and probabilities larger than 1 should be clipped to 1. $\endgroup$ Mar 14, 2018 at 14:38
  • $\begingroup$ Thank you! Could you check whether I am thinking right or not: because linear regression with formula (4.1) depends on \beta_0 and \beta_1, they are influenced by these parameters, therefore \p(X) can be negative. That's a limitation of LR. Hence we need a "clip". Am I thinking correctly? $\endgroup$ Mar 14, 2018 at 14:50
  • $\begingroup$ Yes, that's the basic idea. $\endgroup$ Mar 14, 2018 at 16:43

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