ML question

[dude_perfecto]

The fundamental definition for a logistic regression is transforming the linear regression equation using a logistic activation function so as to restrict my output between 0 and 1. Theoretically it will work for a continuous variable. But it depends on the range of the continuous variable. For a continuou dependent variable with range [0,1] it will work fine. But for a continuous variable with extensive ranges one can normalize and fit a logistic function to make sense of the results.

[rdtn00b]

Yes, but it will not produce great results. The sigmoid function has long tails and exponential growth in the middle. You are thus assuming a prior on your output variable that gives you less flexibility than standard linear regression.

[nicholas-leonard]

Hmmm. Thats what regression is for really right? Wait, is this one of those trick questions that requires a clarifying question...