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Flashcards in 4. Targeting Marketing Interventions Deck (7)
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1

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)

2

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

3

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

4

Expected Value Framework

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

Combines:

  • 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

5

Base Scenario

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

6

Benefit/Cost Matrix

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

7

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.