The second lecture from the Machine Learning course series of lectures. This lecture discusses ROC metric for evaluating machine learning model's performance. In particular, two ways of building ROC are discussed. A link to my github (https://github.com/skyfallen/MachineLearningPracticals) with practicals that I have designed for this course in both R and Python. I can share keynote files, contact me via e-mail: dmytro.fishman@ut.ee.
31. (1, 1, 0, 1, 0)
True labels
TPR = TP/P
FPR = FP/(FP + TN)
TPR
FPR
We would like to evaluate different strictness
levels of our classifier
(0.7,0.6,0.5,0.4,0.2)
32. (1, 1, 0, 1, 0)
True labels
TPR = TP/P
FPR = FP/(FP + TN)
TPR
FPR
What if consider as positive (1) only instances
that were predicted positive with >= 0.7
probability?
(0.7,0.6,0.5,0.4,0.2)
33. (1, 1, 0, 1, 0)
True labels
TPR = TP/P
FPR = FP/(FP + TN)
TPR
FPR
What if consider as positive (1) only instances
that were predicted positive with >= 0.7
probability?
(0.7,0.6,0.5,0.4,0.2)
34. (1, 1, 0, 1, 0)
True labels
TPR = TP/P
FPR = FP/(FP + TN)
TPR
FPR
What if consider as positive (1) only instances
that were predicted positive with >= 0.7
probability?
(0.7,0.6,0.5,0.4,0.2)
What would TPR and
FPR be in this case?
35. (1, 1, 0, 1, 0)
True labels
TPR = TP/P
FPR = FP/(FP + TN)
TPR
FPR
What if consider as positive (1) only instances
that were predicted positive with >= 0.7
probability?
(0.7,0.6,0.5,0.4,0.2)
What would TPR and
FPR be in this case?
>= 0.7 TPR = ?
FPR = ?
36. (1, 1, 0, 1, 0)
True labels
TPR = TP/P
FPR = FP/(FP + TN)
TPR
FPR
What if consider as positive (1) only instances
that were predicted positive with >= 0.7
probability?
(0.7,0.6,0.5,0.4,0.2)
What would TPR and
FPR be in this case?
>= 0.7 TPR = 1/3
FPR = 0/(0 + 2)
48. (1, 1, 0, 1, 0)
True labelsTPR
FPR
(0.7,0.6,0.5,0.4,0.2)
0
AUC is considered to be more
adequate performance
measure than accuracy
AUC = 0.5
AUC of 0.5 means random
guess
49. (1, 1, 0, 1, 0)
True labelsTPR
FPR
(0.7,0.6,0.5,0.4,0.2)
0
AUC is considered to be more
adequate performance
measure than accuracy
AUC = 1
AUC of 0.5 means random
guess
AUC of 1 means perfect
classification
50. (1, 1, 0, 1, 0)
True labelsTPR
FPR
(0.7,0.6,0.5,0.4,0.2)
0
AUC is considered to be more
adequate performance
measure than accuracy
AUC = 1
AUC of 0.5 means random
guess
AUC of 1 means perfect
classification overfitting
🙄
51.
52. References
• Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-
learning)
• Introduction to Machine Learning by Pascal Vincent given at Deep Learning
Summer School, Montreal 2015 (http://videolectures.net/
deeplearning2015_vincent_machine_learning/)
• Welcome to Machine Learning by Konstantin Tretyakov delivered at AACIMP
Summer School 2015 (http://kt.era.ee/lectures/aacimp2015/1-intro.pdf)
• Stanford CS class: Convolutional Neural Networks for Visual Recognition by
Andrej Karpathy (http://cs231n.github.io/)
• Data Mining Course by Jaak Vilo at University of Tartu (https://courses.cs.ut.ee/
MTAT.03.183/2017_spring/uploads/Main/DM_05_Clustering.pdf)
• Machine Learning Essential Conepts by Ilya Kuzovkin (https://
www.slideshare.net/iljakuzovkin)
• From the brain to deep learning and back by Raul Vicente Zafra and Ilya
Kuzovkin (http://www.uttv.ee/naita?id=23585&keel=eng)