I trained a boosting model on the whole dataset (minus the year column) that predicts the Ospef score. The allocation of a student is then basically just iterating through the four houses and pick the one with the maximum score.
As a sanity check of my model I sliced the dataset into a few parts to confirm that we (the Allocation Helm) got worse over time. This wasn’t very rigorous and spending more time would have definitly helped to work out how to mathematically define our degradation. But my testing generally confirmed the downwards trend.
In the end these are my allocations:
Student House A Thought-Talon B Humblescrumble C Serpentyne D Dragonslayer E Humblescrumble F Serpentyne G Dragonslayer H Dragonslayer I Humblescrumble J Thought-Talon K Dragonslayer L Humblescrumble M Humblescrumble N Dragonslayer O Thought-Talon P Humblescrumble Q Thought-Talon R Humblescrumble S Thought-Talon T Humblescrumble
I trained a boosting model on the whole dataset (minus the year column) that predicts the Ospef score. The allocation of a student is then basically just iterating through the four houses and pick the one with the maximum score.
As a sanity check of my model I sliced the dataset into a few parts to confirm that we (the Allocation Helm) got worse over time. This wasn’t very rigorous and spending more time would have definitly helped to work out how to mathematically define our degradation. But my testing generally confirmed the downwards trend.
In the end these are my allocations:
Student House
A Thought-Talon
B Humblescrumble
C Serpentyne
D Dragonslayer
E Humblescrumble
F Serpentyne
G Dragonslayer
H Dragonslayer
I Humblescrumble
J Thought-Talon
K Dragonslayer
L Humblescrumble
M Humblescrumble
N Dragonslayer
O Thought-Talon
P Humblescrumble
Q Thought-Talon
R Humblescrumble
S Thought-Talon
T Humblescrumble