Why do we need natural log of Odds in Logistic Regression? I know what an odds is. It's a ratio of the probability of some event happening to the probability it not happening. So, in the context of classification, the probability that an input feature vector $X$ belongs to class 1 is $p(X)$ then the Odds is:-
$O = \frac{p(X)}{1-p(X)}$
This is what I don't understand. When we have probability, why do we need Odds here at all? 
 A: I think I figured out the answer myself after doing a bit of reading so thought of posting it here. It looks like I got little confused.
So as per my post
$$O = \frac{P(X)}{1-P(X)}.$$
So I forgot to take into account the fact that $P(X)$ itself is the probability given by the logistic function:-
$$P_\beta(X) = \frac{e^{\beta^TX}}{1 + e^{\beta^TX} }.$$
So replacing this in in the equation for $O,$ we get
$$O = \frac{\frac{e^{\beta^TX}}{1 + e^{\beta^TX} }}{1-\frac{e^{\beta^TX}}{1 + e^{\beta^TX} }} = e^{\beta^TX}.$$
So $e^{\beta^TX}$ is nothing but the odds for the input feature vector $X$ to be of a positive class. And with further algebraic manipulation, we can obtain a linear form and the reason for doing this is to be able to interpret the coefficient vector $\beta$ in precise manner. So that algebraic manipulation is basically taking a natural log of the latest form of $O $ ($e^{\beta^TX}$)
i.e.
$$\ln(O) = \ln \left(e^{\beta^TX}\right) =\beta^TX $$
So the expanded form of $\beta^TX$ is:-
$$\ln(O) = \beta_0+\beta_1x_1+\beta_2x_2+\cdots+\beta_nx_n$$
So the real use of this, as I have understood it, is to be able to interpret the coefficients easily while keeping the linear form just like in multiple linear regression. So looking at the latest expanded form of $\ln(O)$ we can say that a unit increase in $x_i$ causes the log of Odds to increase by $\beta_i.$
A: In the equation:
$$
ln (p/1-p)= \beta_0 + \beta_1X
$$
The range of the right hand term is $(-\infty,+\infty)$ while without log the range of the left hand term $(p/1-p)$ is $(0,\infty)$.
Without log, we are using a $(-\infty,+\infty)$ predictor set to map $(0,\infty)$ values, which is not possible. Log transforms the range of $(p/1-p)$ from $(0,\infty)$ to $(-\infty,+\infty)$.
A: 
Why do we need natural log of Odds in Logistic Regression?

The log of odds is logistic regression by definition. So the question is more like, why do we need logistic regression? For this there are several questions that already deal with this
Difference between logit and probit models
Why sigmoid function instead of anything else?
What is the difference between linear regression and logistic regression?
