SVM(support vector Machine)withExplanation.pdf
- 2. • Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, • which is used for Classification as well as Regression problems. • However, primarily, it is used for Classification problems in Machine Learning. SVM
- 5. KEY TERMS • Hyperplane: • SVM identifies the optimal hyperplane that maximizes the margin between classes in the feature space. • Especially useful for scenario where linear separation is not feasible. • Support Vector Machine: • Support Vector are the data points closets to the hyperplane. • They play a pivotal role in determining the position and orientation of the optimal hyperplane.
- 6. Margin: Margin is distance from the decision surface to the closest data point Positive hyperplane + Negative Hyperplane=Margin Suppose D1+D2=M
- 7. GOAL The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.
- 8. Types of SVM Linear SVM: Linear SVM is used for linearly separable data, which means if a dataset can be classified into two classes by using a single straight line, then such data is termed as linearly separable data, and classifier is used called as Linear SVM classifier.
- 9. Non-linear SVM Non-Linear SVM is used for non-linearly separated data, which means if a dataset cannot be classified by using a straight line, then such data is termed as nonlinear data and classifier used is called as Non-linear SVM classifier
- 10. Kernal Trick • SVM employs the Kernal trick to handle no-linear relationships in data by mapping it into a higher dimensional space. • Enables SVM to capture complex patterns and make accurate prediction.
- 12. Drawback • Not suitable for large datasets • Large training time • More features, more complexities • Bad performance on high noise • Choice of kernel • Computationally expensive