Application of logistic regression in machine learning

Logistic regression is a supervised learning technique for classification that estimates the likelihood of a target variable. It works for target variables with binary categories, where 1 represents success or yes and 0 represents failure or no. Therefore, logistic regression predicts the probability of a target variable by building a logistic function. Simply put, logistic regression maps binary data to probabilistic representations of 0 and 1 for classification problems.

The logistic regression model mathematically predicts P(Y=1) as a function of X. It is one of the most basic ML techniques and can be used to solve various classification problems.

Logistic regression is typically used to handle situations with binary target variables, but can be applied to other types of target variables as well. Depending on the number of categories, logistic regression can be divided into several categories.

Binomial or binary is a form of classification in which the dependent variable has only two potential values, 1 or 0. These variables can represent success or failure, yes or no, etc.

Polynomial: The dependent variable in this type of classification can have three or more unordered categories, such as "Class A", "Class B" or "Class C". These categories have no quantitative meaning and are only used to distinguish between different categories.

Ordinal: In this classification, the dependent variable may have three or more potentially ordered categories or types of quantitative significance. For example, these variables might mean "poor" or "good", "very good" or "excellent", with scores ranging from 0 to 2.

Assumptions of logistic regression in machine learning

Before delving into logistic regression, you must understand the following relevant assumptions.

• #The target variable in binary logistic regression must always be binary, with the expected outcome represented by factor level 1.
• The model should not have any multicollinearity, which indicates that the independent variables must be independent of each other.
• The model must contain relevant variables.

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