204.6.7 Soft Margin Classification – Noisy Data and Validation

What happens if the data is noisy with SVM?
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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.

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