Explain different methods that can be used to evaluate and compare the accuracy of different classification algorithm.
  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.

  1. Speed :

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

  1. Robustness:

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

  1. Scalability:

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

  1. Interpretability:

It refers to what extent the classifier or predictor understands.

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