Informations Générales
Type de Modèle
RandomForestRegressor
Version
1.0
Date de Création
2025-09-21T11:07:42.926661
Module Python
sklearn.ensemble._forest
Taille du Modèle
26.0 MB
Nombre de Features
17
MĂ©triques de Performance
0.916
Accuracy (R²)
0.090
RMSE
0.220
MAE
0.920
R² Score
Capacités du Modèle
Feature Importances
âś“ Disponible
Coefficients
âś— Non disponible
Prédiction Probabiliste
âś— Non disponible
Variables d'Entrée (17 features)
year_built
number_of_buildings
number_of_floors
property_gfa_total
property_gfa_parking
second_largest_property_use_type_gfa
third_largest_property_use_type_gfa
multiusage
steam
electricity
natural_gas
neighborhood_id
building_type_id
largest_property_use_type_id
primary_property_type_id
second_largest_property_use_type_id
third_largest_property_use_type_id
Importance des Variables
| Variable | Importance | Pourcentage | Visualisation |
|---|---|---|---|
| property_gfa_total | 0.4159 | 41.59% | |
| primary_property_type_id | 0.1205 | 12.05% | |
| largest_property_use_type_id | 0.0907 | 9.07% | |
| year_built | 0.0760 | 7.60% | |
| second_largest_property_use_type_gfa | 0.0665 | 6.65% | |
| number_of_floors | 0.0649 | 6.49% | |
| natural_gas | 0.0444 | 4.44% | |
| neighborhood_id | 0.0368 | 3.68% | |
| property_gfa_parking | 0.0209 | 2.09% | |
| second_largest_property_use_type_id | 0.0206 | 2.06% |