Logistic regression

Logistic regression is a statistical and machine learning technique widely used to solve binary classification problems. This algorithm predicts the probability that the outcome variable (dependent variable) belongs to a specific class through a linear combination of independent variables. Although it is primarily applied in binary classification with labels of 0 or 1, it can be extended to multiclass classification as well.

• Logistic: Used in scenarios requiring dichotomous outcomes,
  • such as pass/fail, success/failure, survival/death, or true/false.
• Regression analysis: Predicts future outcomes based on past trends.
  • Since logistic regression analysis has a categorical dependent variable, it is closer to a classification model.

Functions Used

Function	Formula
Sigmoid 1/(1+e-x)
Hyperbolic Tangent tanh(x)

Types of Regression Analysis

• Simple Regression Analysis: Single independent variable
• Multiple Regression Analysis: Two or more independent variables

Advantages and Disadvantages

• Advantages: Simple to implement and easy to interpret.
  • It has a relatively low risk of overfitting and is effective for binary classification.
• Disadvantages: Performs poorly with data that lacks a linear relationship.
  • It is challenging to apply directly to multiclass problems, where techniques like softmax regression are often required.

See Also

• Regression Analysis
• Linear Regression