Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable (in which there are only two possible outcomes)*You can think like entropy in decision trees. If probability of stiuation is binary there is no relationship with outcome.

The function that is included to your post about theory. In an applicaton you are going to use this term for it.

$w \cdot x = \sum_{i=1}^n w_i x_i$ or $F(p) = \sum_{i=1}^n w_ix_i$

And it will give you a curve shape like half of gaussian curve

I hope it helps

*"https://www.medcalc.org/manual/logistic_regression.php"

Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. The outcome is measured with a dichotomous variable in which there are only two possible outcomes. You can think like entropy in decision trees. If probability of situation is binary there is no relationship with outcome.

The function that is included to your post about theory. In an application you are going to use this term for it.

$w \cdot x = \sum_{i=1}^n w_i x_i$ or $F(p) = \sum_{i=1}^n w_ix_i$

And it will give you a curve shape like half of Gaussian curve