Dennis Collaris, Jarke J. van Wijk
Eindhoven University of Technology
Data
Black
Box
Subject
80% risk
ML is often applied as a black box.
Strategy A
Strategy B
The basic principle
The basic principle
ID | Name | Age | Sex | Product | Branch | ... |
---|---|---|---|---|---|---|
1 | 💤 | 💤 | 💤 | 🔥 | 💤 | ... |
2 | 🔥 | 💤 | 💤 | 💤 | 🔥 | ... |
3 | 💤 | 🔥 | 💤 | 🔥 | 🔥 | ... |
... | ... | ... | ... | ... | ... | ... |
ID | Name | Age | Sex | Product | Branch | ... |
---|---|---|---|---|---|---|
1 | Alice | 28 | F | Health | Zekur | ... |
2 | Bob | 57 | M | Car | FBTO | ... |
3 | Chad | 34 | M | Life | Intrpls | ... |
... | ... | ... | ... | ... | ... | ... |
2D projection
StrategyMap
feature contribution (LIME)
Method 1: Heat map cluster analysis
Data
Model
Method 2: Density plots
Data
Model
All data
Selection
Method 3: Decision trees
Saved clusters  →
DT for selected cluster →
Performance comparison →
Demo of the system
ID | Name | Age | Sex | Product | Branch | Risk? |
---|---|---|---|---|---|---|
1 | Alice | 28 | F | Health | Zekur | - |
2 | Bob | 57 | M | Car | FBTO | - |
3 | Charlie | 34 | M | Life | Interpolis | ✓ |
... | ... | ... | ... | ... | ... | ... |