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Confusion Matrix

A confusion matrix allows visualization of the performance of a model

  • Accuracy
    • (correct prediction) / (all predictions)
    • (TP + TN) / (TP + TN + FP + FN)
  • Error rate
    • (false predictions) / (all predictions)
    • ​​(FP + FN) / (TP + TN + FP + FN)
  • TP rate
    • (positive labels) / (all positives)
    • TP / (TP + FN)
  • FP rate
    • (false positives) / (all negatives) 
    • FP / (FP + TN)


Receiver Operating Characteristic (ROC)

  • The ROC graph shows the entire space of performance possibilities for a given model, independent of class balance
  • It depicts relative trade-offs that a classifier makes between benefits (true positives) and costs (false negatives)
  • The line ranking (0,0) to (1,1) is the strategy of guessing randomly


Area under the curve (AUC)

  • AUC is the probability that the model will rank a randomly chosen positive case higher than a negative case
  • AUC is useful when a single number is needed to summarize performance, or when nothing is known about the operating conditions


Expected Value Framework

The expected value framework is an analytical tool that is extremely helpful in organizing thinking about data-analytic problems. 


  • Structure of the problem
  • Elements of the analysis that can be extracted from the data
  • Elements of the analysis that need to be acquired from business knowledge


Base Scenario

A base scenario is needed to compare the outcomes of the expected value framework.


Benefit/Cost Matrix

  • Summarizes the benefits and costs of each potential outcome
  • Always comparing with a base scenario


Expected Profit

By multiplying the costs and benefits of the cost/benefit matrix with the number of observations in the confusion matrix we can calculate the expected profit.