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Learning activity 6
Learn the required content of this unit, and conduct your research to answer the following questions in this discussion forum:
Describe how the confusion matrix is used for model evaluation.
Describe the definition of accuracy, precision, recall, sensitivity, specificity, True positive rate, False positive rate.
Synthesize real-life examples where you would rely on each one of these measures to evaluate prediction, or classification, performance of your model.
What is a Lift chart? Explain in what situations you would use it?
What is ROC chart? Explain in what situations you would use it?
Videos
Topic Access
Confusion Matrix (7 min) https://www.youtube.com/watch?v=Kdsp6soqA7o
Train, Test, & Validation Data Sets (7 min) https://www.youtube.com/watch?v=Zi-0rlM4RDs
ROC and AUC, Clearly Explained (16 min) https://www.youtube.com/watch?v=4jRBRDbJemM
Evaluating Classifiers: Gains and Lift Charts ( 14 min) https://www.youtube.com/watch?v=1dYOcDaDJLY
Articles
Medium Topic Access
Excercise Understanding Confusion Matrix in R https://www.datacamp.com/community/tutorials/confusion-matrix-calculation-r
Exercise with Answer Model Evaluation https://www.r-exercises.com/2016/12/02/model-evaluation-exercise-1/
Exercise with Answer Model Evaluation https://www.r-exercises.com/2016/12/22/model-evaluation-2/
Tutorial Lift Charts https://www.ibm.com/support/knowledgecenter/de/SSLVMB_24.0.0/spss/tutorials/mlp_bankloan_outputtype_02.html
Tutorial Generate ROC Curve Charts for Print and Interactive Use https://cran.r-project.org/web/packages/plotROC/vignettes/examples.html
