Linear Models

Linear models are a foundational statistical and machine learning technique that assumes a linear relationship between input features and a target variable, used for tasks like regression and classification. They include methods such as linear regression, logistic regression, and ridge regression, which model outcomes as a weighted sum of inputs plus an error term. These models are valued for their simplicity, interpretability, and efficiency in handling large datasets.

Also known as: Linear Regression Models, Generalized Linear Models, GLMs, Linear Predictive Models, Linear Statistical Models

Why learn Linear Models?

Developers should learn linear models for predictive analytics, especially when interpretability is crucial, such as in finance, healthcare, or business intelligence where understanding feature impacts is key. They are ideal for baseline modeling in machine learning projects, handling linear relationships effectively, and are computationally efficient for large-scale data, making them suitable for real-time applications or initial data exploration.

Learning Resources

• docs: Scikit-learn Documentation: Linear Models
• course: Coursera: Machine Learning by Andrew Ng
• book: Introduction to Statistical Learning
• tutorial: Khan Academy: Linear Regression
• video: YouTube: StatQuest with Josh Starmer - Linear Regression