Regression Model
A regression model is a statistical tool used to predict a continuous outcome variable based on one or more predictor variables. These models establish relationships between dependent and independent variables by fitting a mathematical function to the observed data. Common types of regression models include linear regression, polynomial regression, and logistic regression, each tailored for specific data distributions and relationships. For instance, linear regression, introduced in the early 1800s, assumes a straight-line relationship, while polynomial regression accounts for curvature by incorporating higher-order terms.
https://en.wikipedia.org/wiki/Regression_analysis
In modern machine learning and data science, regression models play a pivotal role in tasks like sales forecasting, pricing analysis, and environmental modeling. Techniques like regularization—implemented in methods like ridge regression or lasso regression—help prevent overfitting by penalizing overly complex models. Moreover, regression models are used in ensemble methods such as gradient boosting and random forests, where multiple models work together to improve predictive accuracy.
https://en.wikipedia.org/wiki/Regularization_(mathematics)
The practical implementation of regression models has been revolutionized by libraries like scikit-learn and TensorFlow, which provide pre-built algorithms for fitting and evaluating regression models efficiently. These tools automate critical steps, such as feature scaling and hyperparameter tuning, ensuring optimal model performance. Applications span various industries, from healthcare, where regression predicts disease progression, to finance, where it forecasts market trends. The adaptability and computational efficiency of regression models make them an essential component of predictive analytics.