Talk given at PAPIs Latam 2019.

1. game theory and
model interpretability
PAPIs 2019
gabriel cypriano

2. We were born in 2018, with the mission
of transforming the way of doing business
in the Brazilian real estate market
through technology.
who is

3. We were born in 2018, with the mission
of transforming the way of doing business
in the Brazilian real estate market
through technology.
who is
8M
listings
75K
searches per hour
40M
per month
PROPERTIES
–
SEARCHES
–
VISITS
–

4. For each prediction, how do we explain
(potentially to a layman) how the
model arrived at its output?
model interpretability

5. 3 friends planning a night out3 friends planning a night out

6. if each one
takes an Uber
due: R$ 34
Black: R$ 14
Yellow: R$ 10
Green: R$ 10

7. if they share
an Uber
due: R$ 20
Black + Yellow + Green: R$ 20

8. A solution concept in cooperative
Game Theory. Named in honor of
Lloyd Shapley, who introduced it
in 1953.
shapley value

9. desired
properties
efficiency
cost reduction for every
player who collaborates
symmetry
same cost before → same
afterwards
null player
no collaboration → no
cost reduction
monotonicity
if multiple games → final
cost = sum of costs

10. data needed
for shapley
value
Black + Yellow: R$ 18
Black + Green: R$ 18
Yellow + Green: R$ 12

11. shapley values 10 5 5
20
12
18
18
10
10
14
input data
108 2
108 2
14
14
10
10
4 2
2 4
8 2
8 2

12. Cost before vs after collaborating
R$ 14
R$ 10
R$ 10
→ R$ 10
→ R$ 5
→ R$ 5
R$ 4 → R$ 4

13. how to translate
shapley values to
machine learning?
feature importances ← player's shapley values
predicted value ← total game cost
feature
prediction ← cooperative game
← player

14. Local explanations (with SHAP values)

15. Local explanations (with SHAP values)

16. Global explanations (with SHAP values)

17. Global explanations (with SHAP values)

18. SHAP
properties
consistency
changing a model so a feature
has larger impact never
decreases its importance
missingness
features missing in the
input have no impact
local accuracy
the explanation model's
prediction matches the
original model's

19. Python package: SHAP (github.com/slundberg/shap)

20. Python package: SHAP (github.com/slundberg/shap)

21. Python package: SHAP (github.com/slundberg/shap)

22. Python package: SHAP (github.com/slundberg/shap)

23. Python package: SHAP (github.com/slundberg/shap)

24. Python package: SHAP (github.com/slundberg/shap)

25. about.me/gabrielcs
vagas.grupozap.com
thank you!