# 203.6.7 Soft Margin Classification – Noisy Data and Validation

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January 27, 2017
Predictive Modeling & Machine Learning, R
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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