Regression is a fundamental concept in machine learning and statistics used to model the relationship between a dependent variable and one or more independent variables. Unlike classification, which predicts discrete categories, regression focuses on predicting continuous values. For example, predicting house prices based on features like size, location, and number of bedrooms involves regression. Common algorithms include linear regression, polynomial regression, and advanced methods like ridge regression, lasso regression, and elastic net. These techniques aim to minimize the difference between the predicted and actual values, often quantified using metrics like mean squared error (MSE) or mean absolute error (MAE).
https://en.wikipedia.org/wiki/Regression_analysis
Regression is widely applied across domains, from forecasting stock prices in finance to predicting temperature variations in weather modeling. Tools such as scikit-learn and TensorFlow, introduced in 2007 and 2015, respectively, offer extensive frameworks for implementing regression models. In healthcare, regression is instrumental in estimating patient outcomes based on clinical data. The flexibility of regression allows it to accommodate linear and non-linear relationships, making it versatile for different types of datasets. Moreover, feature engineering and selection are critical steps in improving the performance of regression models.
https://scikit-learn.org/stable/
Advanced regression methods like support vector regression (SVR), decision tree regression, and ensemble techniques like random forests and gradient boosting further enhance predictive accuracy, especially with high-dimensional or noisy datasets. Additionally, neural networks have extended the capabilities of regression to handle complex, non-linear patterns in data, often seen in fields like image analysis and natural language processing. Despite its advantages, regression requires careful handling of overfitting, multicollinearity, and outliers to ensure robust model performance.