Understanding the testing matrix

In this section, we will look at some of the widely used testing matrices that we can use in order to get an idea about how good or bad our trained model is. This testing score gives us a fair idea about which model achieves the highest accuracy when it comes to the prediction of the 25% of the data.

Here, we are using two basic levels of the testing matrix:

  • The mean accuracy of the trained models
  • The ROC-AUC score

The Mean accuracy of the trained models

In this section, we will understand how scikit-learn calculates the accuracy score when we use the scikit-learn function score() to generate the training accuracy. The function score() returns the mean accuracy. More precisely, it uses residual standard error. Residual ...

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