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Explain different methods that can be used to evaluate and compare the accuracy of different classification algorithms?
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1. Accuracy:

  • Accuracy of classifier refers to the ability of classifier.
  • It predicts the class label correctly and the accuracy of the predictor refers to how well a given predictor can guess the value of predicted attribute for a new data.

2. Speed :

  • This refers to the computational cost in generating and using the classifier or predictor.

3. Robustness:

  • It refers to the ability of classifier or predictor to make correct prediction from given noisy data.

4. Scalability:

  • Scalability refers to the ability to construct the classifier or predictor efficiently; given large amount of data.

5. Interpretability:

  • It refers to what extent the classifier or predictor understands.
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