Logistic Regression is a statistical method used for binary classification, where the dependent variable is categorical and has only two possible outcomes (usually coded as 0 and 1). Despite its name, logistic regression is a classification algorithm rather than a regression algorithm. Here’s a comprehensive overview of logistic regression:
1. Basic Concept:
a. Binary Classification:
- Logistic regression is used when the outcome variable is binary (e.g., spam or not spam, yes or no).
b. Log-Odds Transformation:
- The logistic function is used to transform a linear combination of input features into a value between 0 and 1, representing the probability of belonging to the positive class.
2. Parameters and Learning:
a. Parameters θ:
- The goal is to learn the optimal parameters that maximize the likelihood of the observed data.
b. Cost Function:
- The cross-entropy (log loss) function is commonly used to measure the difference between predicted and actual values.
