svr

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.