Link to the previous post : https://course.dvanalyticsmds.com/204-6-6-practice-kernel-non-linear-classifier/
Soft Margin Classification – Noisy data
Noisy data
- What if there is some noise in the data.
- What id the overall data can be classified perfectly except few points.
- How to find the hyperplane when few points are on the wrong side.
Soft Margin Classification – Noisy data
- The non-separable cases can be solved by allowing a slack variable(x) for the point on the wrong side.
- We are allowing some errors while building the classifier.
- In SVM optimization problem we are initially adding some error and then finding the hyperplane.
- SVM will find the maximum margin classifier allowing some minimum error due to noise.
- Hard Margin -Classifying all data points correctly.
- Soft margin – Allowing some error.
SVM Validation
- SVM doesn’t give us the probability, it directly gives us the resultant classes.
- Usual methods of validation like sensitivity, specificity, cross validation, ROC and AUC are the validation methods.
The next post is about SVM advantages and disadvantages applications.
Link to the next post : https://course.dvanalyticsmds.com/204-6-8-svm-advantages-disadvantages-applications/
