Interesting Facts About SVM (Support Vector Machines) in Machine Learning
- Is a supervised machine learning algorithm
- It is used for classification as well as the regression problems.
- These are the supervised ML models with associated learning algorithms that analyze data for classification and regression analysis.
- It is a linear model for classification and regression analysis.
- It is capable of solving linear and non-linear problems.
- It works extremely well for many practical problems.
- The idea behind the SVM is fairly simple. It creates a line or a hyperplane that separates the data into classes.
- SVMs are powerful tools to identify predictive models and classifiers not only because they accommodate sparse data very well but also because they can classify groups of data or create predictive rules.
- SVM types: Linear SVM, Non-Linear SVM, Use of Dot Product in SVM, Polynomial Kernel, Sigmoid Model, Anova Kernel.
- The goal of SVM (Support Vector Machine) is to find a decision boundary which splits the data into binary classes.
To conclude, the SVM (Support Vector Machine) is a supervised machine learning algorithm used for both classification and regression. And for that purpose SVM uses classification algorithms for two group classification problems.