What are some common regression algorithms

Regression algorithms are essential in supervised machine learning, used to model the relationship between input features and a continuous target variable. Here are some commonly used regression algorithms:

Linear Regression:

•     Description: Models the relationship as a linear equation between the input features and the target variable.
  
•     Use Case: Predicting house prices based on size, location, and other features.
  
•     Key Feature: Simple and interpretable, works well with linearly separable data.
  

Polynomial Regression:

•     Description: Extends linear regression by fitting a polynomial curve to the data.
  
•     Use Case: Captures non-linear relationships, such as modeling growth rates or complex trends.
  
•     Key Feature: Handles non-linearity by transforming features into polynomial terms.
  

Ridge and Lasso Regression:

•     Description: Variants of linear regression with regularization to prevent overfitting.
  
•     Ridge: Adds L2 regularization (penalizes large coefficients).
  
•     Lasso: Adds L1 regularization (can shrink some coefficients to zero).
  
•     Use Case: High-dimensional datasets with multicollinearity or sparse features.
  
•     Key Feature: Helps improve generalization in predictive models.
  

Logistic Regression:

    Description: Despite its name, it is commonly used for classification but can handle specific regression problems involving probabilities.

    Use Case: Binary classification tasks like spam detection.

Support Vector Regression (SVR):

•     Description: Uses a margin of tolerance to fit the best hyperplane for regression tasks.
  
•     Use Case: Works well for small to medium-sized datasets with complex relationships.
  
•     Key Feature: Robust to outliers and effective for non-linear data.
  

Decision Tree Regression:

•     Description: Splits data into subsets based on feature values, forming a tree structure.
  
•     Use Case: Predicts continuous values while handling non-linear data.
  
•     Key Feature: Easy to interpret and handle mixed types of data.
  

Each algorithm has its strengths and is chosen based on the dataset and problem complexity.