The motivation of this post is to aide the understanding of the classification technique by explaining the evaluation tool for classification experiments: the confusion matrix.  The name certainly does not inspire confidence in understanding, but let’s set that aside for a moment.

Lets consider a simple classification example: a machine learning experiment to predict if a customer will churn.

Recall that with supervised machine learning, a data set of examples (called the training set) is used for the machine to learn examples and explanatory relationships.  The confusion matrix is a 2 x 2 grid with 4 measures that illustrate how effective the model performed on the test set of data. The four metrics are

  • True Positive
  • True Negative
  • False Positive
  • False Negative

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