Support Vector Machine In Regression

Support Vector Machine In Regression

Support Vector Machine In Regression 

Description    

The Support Vector Machine (SVM) approach for regression is a machine learning technique that seeks to identify a hyperplane that closely resembles the regression function. It reduces deviations (epsilon-insensitive loss) within a predetermined margin. SVM is less prone to overfitting and is capable of handling high-dimensional spaces. However, it may be computationally demanding and necessitate careful kernel and hyperparameter tweaking. For precise prediction tasks, SVM regression is a frequently used method.

Why to use     

  1. Effective in high-dimensional spaces
  2. Robust against outliers
  3. Flexibility in kernel selection
  4. Control over regularization

When to use   

  1. Non-linear relationships
  2. Limited training data
  3. Robustness to outliers
  4. High-dimensional feature space
  5. Clear margin of separation

When not to use

  1. Large datasets
  2. Linear relationships
  3. High interpretability requirement
  4. Noise-dominated datasets
  5. Time efficiency

Prerequisites    

  1. Pre-processing and feature scaling
  2. Numerical features
  3. Handling outliers
  4. Regularization parameter
  5. Linear or non-linear relationships
  6. Model evaluation
  7. Computational resources

Input            

Any Continuous Data

 Output

  1. AIC
  2. Adjusted R Square
  3. BIC
  4. R Square
  5. RMSE
Statistical Methods  Used
  1. Convex Optimization
  2. Margin Maximization
  3. Loss Function
  4. Regularization
  5. Kernel Functions
  6. Support Vector Selection
Limitations
  1. Sensitivity to outliers
  2. Difficulty in handling large datasets
  3. Determination of appropriate kernel function and parameters
  4. Lack of probabilistic outputs
  5. Limited effectiveness with high dimensional data

Support Vector Machine (SVM) in regression is a machine learning algorithm for predicting continuous numerical values. In SVM regression, the goal is to find a hyperplane that approximates the underlying regression function. Support vectors determine the hyperplane; they are the data points closest to the decision boundary.

The equation of the hyperplane is given by: w * x + b = 0

  • w is a vector perpendicular to the hyperplane and represents the weights of the features.
  • x is the input vector representing a data point.
  • b is the bias term.

To make predictions, you can use the sign of the equation w * x + b to determine the class to which a data point belongs. When w * x + b is positive, it means the data point belongs to one class, and when it is negative, it indicates that it belongs to the other class.

The key idea of SVM regression is to minimize the deviations between the predicted and actual values within a specified margin. SVM does not aim to fit all data points precisely. Instead, it seeks a balance between minimizing deviation and maximizing the margin around the hyperplane.
In summary, SVM regression is an algorithm for predicting continuous numerical values. SVM actively finds a hyperplane that approximates the regression function and effectively balances deviations within a margin. Additionally, it utilizes the kernel trick to handle non-linear relationships. Various domains, including finance, economics, and engineering, widely use SVM regression for accurate prediction tasks.

    • Related Articles

    • Support Vector Machine

      Support Vector Machine Description SVM belongs to the family of supervised classification algorithms. It uses the kernel trick to transform data into the desired format and creates an optimum boundary between the possible outputs. SVM performs ...

    • Machine Learning Concepts

      Advanced Entity Extraction Advanced entity extraction, also known as entity recognition, is used to extract vital information for natural language processing (NLP). It is widely used for finding, storing and sorting textual content into default ...

    • k Nearest Neighbor Regression

      k Nearest Neighbor Regression Description k Nearest Neighbor (KNN) Regression enables you to predict new data points based on the known classification of other points. In kNN, we take a bunch of labeled points and then learn how to label other ...

    • Regression

      Regression is predictive modeling. It is a statistical method, used in finance, investment, and other disciplines, that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a ...

    • Extreme Gradient Boost Regression (XGBoost)

      Extreme Gradient Boost Regression (XGBoost) Description Extreme Gradient Boost (XGBoost) Regression is a Decision tree-based ensemble algorithm that uses a gradient boosting framework. Why to use Predictive Modeling When to use When high execution ...