Kaggle
Winner
Presentation
Template
Agenda
1. Background
2. Summary
3. Previous/Other Contributions
4. Feature selection & engineering
5. Training methods
6. Important findings
7. Simple model
Background
• [ Your professional/academic background ]
• [ Prior experience (if any) that helped you
succeed in this competition ]
Previous/Other
contributions
• Describe major previous contributions that is
most relevant to your work/goals
Summary
• [ Training methods you used eg. Convolutional
Neural Network, XGBoost ]
• [ Most important features ]
• [ The tools you used ]
• [ How long does it take to train your model? ]
Features
Selection /
Engineering
• [ Most important features. Recommend variable
importance plot – see next slide ]
• [ Attributing progression on Leaderboard to
techniques. Recommend LB Performance
Chart – see slide after next ]
• [ Outline any important feature transformations ]
Features
Selection /
Engineering
Variable Importance Plot
Important Feature #28
Important Feature #27
Important Feature #26
Important Feature #25
Important Feature #24
Important Feature #23
Important Feature #22
Important Feature #21
Important Feature #20
Important Feature #19
Important Feature #18
Important Feature #17
Important Feature #16
Important Feature #15
Important Feature #14
Important Feature #13
Important Feature #12
Important Feature #11
Important Feature #10
Important Feature #9
Important Feature #8
Important Feature #7
Important Feature #6
Important Feature #5
Important Feature #4
Important Feature #3
Important Feature #2
Important Feature #1
0,7
0,75
0,8
0,85
0,9
0,95
1
Features
Selection /
Engineering
Leaderboard Performance Chart
Features
Selection /
Engineering
• [ Relationship between the most important
features and the target variable. Recommend
using partial plots – see next slide ]
Partial Plot of Important Feature #1
3,6
Features
Selection /
Engineering
Target Variable
3,5
3,4
3,3
3,2
3,1
500
700
900
1100
1300
1500
Important Feature #1
1700
1900
2100
2300
3,6
Partial Plot of Important Feature #2
Features
Selection /
Engineering
Target Variable
3,5
3,4
3,3
3,2
3,1
500
700
900
1100
1300
1500
Important Feature #2
1700
1900
2100
2300
Training Methods
• [ Training methods you used ]
• [ Did you ensemble? How did you weight
different models? ]
Important and
Interesting
Findings
• [ What set you apart from others in the
competition? ]
• [ Interesting relationships in the data that don't
fit in the sections above. Recommend showing
interesting visualizations – see next slide. ]
Important and
Interesting
Findings
Interesting visualization found when exploring the data
Simple Model
• [ Outline a subset of features that would get 9095% of your final performance ]
• [ If you used an ensemble, was there a single
classifier that did most of the work? Which one?
]
• [ What would the simplified model score be? ]