Linear Regression In 5 Minutes

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Linear Regression is a fundamental and widely-used supervised learning algorithm in machine learning and statistics for predicting a continuous target variable based on one or more independent variables. This algorithm assumes a linear relationship between the input features (predictors) and the target variable, represented by the equation. In simple linear regression, there is a single predictor, while multiple linear regression involves multiple predictors. The goal is to find the optimal parameters (coefficients) that minimize the cost function, typically the Mean Squared Error (MSE), using methods like Ordinary Least Squares (OLS) or Gradient Descent. Linear regression can also be extended to Ridge Regression and Lasso Regression to handle multicollinearity and perform feature selection through regularization techniques. Assumptions of linear regression include linearity, homoscedasticity, independence of errors, and normality of residuals. These assumptions are critical for valid inference and can be checked using diagnostic plots and statistical tests. Linear regression is popular due to its simplicity, interpretability, and efficiency in estimating relationships between variables. It is used in various applications, including predictive modeling, trend analysis, econometrics, and time series forecasting. Its implementation is supported in major ML libraries such as scikit-learn, TensorFlow, and PyTorch, making it a foundational tool for both data scientists and machine learning practitioners.

[alextran]

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