Support Vector Machine

Support Vector Machine

Support Vector Machine

Description

  • SVM belongs to the family of supervised classification algorithms. It uses the kernel trick to transform data into the desired format and creates an optimum boundary between the possible outputs.
  • SVM performs remarkably even if there is limited available data for analysis.

Why to use

To classify text into the possible categories.

When to use

When textual data needs to be classified.

When not to use

When the dataset does not contain textual data.

Prerequisites

  • The dataset should not contain any missing (NaN) values.
  • The dataset should contain at least one categorical and one textual variable.

Input

Textual Data

Output

Classified data with predicted labels

Statistical Methods used

  • Accuracy
  • Sensitivity
  • Specificity
  • F-score
  • Confusion Matrix

Limitations

  • Choosing a good kernel
  • Takes long training time in case of large datasets
  • Not easy to tune hyperparameters (Gamma and Penalty Parameter)

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