SVR - Support Vector Regression
Description: SVR uses support vector machines for regression tasks by finding a function that deviates from the actual target values by a value no greater than a specified margin.
Key Points:
- Effective in high-dimensional spaces.
- Robust to outliers.
- Sensitive to the choice of kernel and regularization parameter.
Applications: Time series prediction, stock price forecasting, real estate valuation.