Regression Techniques

Regression Techniques

• Linear Regression: A simple linear model that predicts a continuous dependent variable based on one or more independent variables. Assumes a linear relationship between variables.
• Ridge Regression: A modified linear regression that adds a penalty term to the loss function to prevent overfitting. Useful when dealing with highly correlated predictor variables. This approach shrinks the model coefficients, which is a powerful regularization technique.
• Lasso Regression: Similar to ridge regression, but uses a different penalty term that can lead to feature selection by forcing some coefficients to be exactly zero. This helps to reduce model complexity.
• Support Vector Regression (SVR): Uses support vectors to define a regression model. Tries to find the best hyperplane that minimizes the error within the specified margin. More effective if the data has a non-linear relationship.
• Decision Trees Regression: A non-parametric approach that builds a tree-like model of decisions and their consequences to predict a continuous target value. Uses a tree-like structure to make predictions.
• Random Forest Regression: An ensemble method that combines multiple decision trees to make a more accurate prediction. Aggregates the predictions from a collection of trees by voting.
• Gradient Boosting Regression: Another ensemble method that sequentially builds models, each one correcting the errors of the previous ones. Usually produces strong performance due to its ability to sequentially learn and refine predictions.
• Neural Networks Regression: Utilizes artificial neural networks with multiple layers to model complex non-linear relationships between variables, allowing highly flexible and adaptive learning of complex patterns in the data.

Explore various regression techniques used in statistics and machine learning. This quiz covers linear regression, ridge regression, lasso regression, support vector regression, and decision trees regression. Test your knowledge on how these methods differ and their applications in data analysis.