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203.6.7 Soft Margin Classification – Noisy Data and Validation

Soft Margin Classification – Noisy data

In previous section, we studied about Kernel – Non Linear Classifier

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.


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