# Logistic Regression

## Description

**Logistic regression** classifier is a statistical model that uses the logistic function to model a categorical target variable.

In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function.

Logistic regression does not require a linear relationship between inputs and output variables. This is due to applying a nonlinear log transformation to the odds ratio. If the probability is greater than *0.5*, the predictions will be classified as class *1*. Otherwise, class *0* is assigned.

The solvers are the optimization models used to minimize the cost function.

It is possible to use the following solvers:

**newton-cg****lbfgs****sag****saga**

**lbfgs**, **sag** and **newton-cg** solvers converge faster for some high-dimensional data.

The **lbfgs** solver is recommended for use for small datasets because performance drops with large datasets.

**sag** and **saga** are faster than other solvers for large datasets when both the number of samples and the number of features is large.

## Properties

Logistic Regression is an efficient algorithm for finding linear-separated surfaces. It can scale to high volumes of data, and it can perform relatively well even with a reduced number of training samples. It natively provides outputs as probabilities.

## Hyperparameters

The hyperparameters for this model type are:

**Optimization problem algorithm****Inverse of regularization strength****Stop condition tolerance****Class weight**