Linear and logistic regression are two fundamental machine learning algorithms with distinct purposes and applications. Linear regression predicts continuous numerical values using a straight line relationship, while logistic regression predicts probabilities and classifications using an S-shaped curve. Understanding their differences is crucial for choosing the right approach for your data science problems.
Linear regression is used to predict continuous numerical values by finding the best-fit straight line through data points. The algorithm minimizes the sum of squared residuals, which are the vertical distances between actual data points and the predicted line. This makes it perfect for problems like predicting house prices based on size, forecasting sales revenue, or estimating temperature changes over time.
Logistic regression solves classification problems by using the sigmoid function to map any real number to a probability between 0 and 1. Unlike linear regression's straight line, logistic regression creates an S-shaped curve that naturally bounds predictions within the probability range. The decision boundary is typically set at 0.5 probability, making it ideal for binary classification tasks like email spam detection, medical diagnosis, or student pass-fail prediction.