Linear Regression
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Linear regression is a machine learning algorithm used to model the linear relationship between independent variables and dependent variables. This model can predict the value of target variables, for example, forecasting rainfall in a specific region based on temperature and humidity data. Linear regression is typically based on the least squares method, which determines the best-fit line by minimizing the sum of squared errors between observed values and predicted values. The algorithm is widely applied in data analysis and predictive modeling. Implementation often involves matrix operations to solve normal equations or gradient descent optimization to minimize the cost function. Key components include feature normalization, hypothesis function formulation (hθ(x) = θ₀ + θ₁x₁ + ... + θₙxₙ), and evaluation metrics like R-squared to assess model performance.
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