The logit function maps probabilities to values over the entire real number range. Thus, the probability of an event/outcome/success to be true $(y=1)$, given the set of predictors $x_i$, which is our data, is written as

$$logit(P(y=1|x_i))= \beta_0+ \beta_1x_1+ \beta_2x_2+ ... +\beta_kx_k\text{.}$$For a matter of simplification, we express the inverse of the function above as

$$\phi(\eta) = \frac{1}{1+e^{-\eta}}\text{,}$$where $\eta$ is the linear combination of coefficients $(\beta_i)$ and predictor variables $(x_i)$, calculated as

$$\eta = \beta_0+ \beta_1x_1+ \beta_2x_2+ ... +\beta_kx_k$$.

The parameters $(\beta_i)$ of the logit model are estimated by the [**method of maximum likelihood**](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation. However, there is no closed-form solution, so the maximum likelihood estimates are obtained by using iterative algorithms such as Newton-Raphson, iteratively re-weighted least squares or gradient descent, among others.

The output of the sigmoid function is interpreted as the probability of a particular observation belonging to class 1. It is written as $\phi(\eta)=P(y=1|x_i,\beta_i)$, the probability of success $(y=1)$ given the predictor variables $x_i$ parameterized by the coefficients $\beta_i$. For example, if we compute $\phi(\eta)=0.65$ for a particular observation, this means that the chance that this observation belongs to class 1 is 65%. Similarly, the probability that this observation belongs to class 2 is calculated as

$$\phi(\eta)=P(y=0|x_i,\beta_i)= 1 - P(y=1|x_i,\beta_i)=1-0.65=0.35$$or 35%. For class assignment the predicted probability is then converted into a binary outcome via a unit step function:

$$ \hat y = \begin{cases} 1, & \text{if $\phi(\eta) \ge$ 0.5} \\ 0, & \text{otherwise} \end{cases} $$**Citation**

The E-Learning project SOGA-Py was developed at the Department of Earth Sciences by Annette Rudolph, Joachim Krois and Kai Hartmann. You can reach us via mail by soga[at]zedat.fu-berlin.de.

You may use this project freely under the Creative Commons Attribution-ShareAlike 4.0 International License.

Please cite as follow: *Rudolph, A., Krois, J., Hartmann, K. (2023): Statistics and Geodata Analysis
using Python (SOGA-Py). Department of Earth Sciences, Freie Universitaet Berlin.*