Probabilistic Calibration in a Few-Shot Domain Adaptation Setting
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Data science and AI (MPDSC), MSc
Publicerad
2024
Författare
Adamsson, Oscar
Röst, Jonas
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis investigates the application of probabilistic machine learning models
within a Few-Shot Domain Adaptation (FSDA) setting to address covariate shifts
induced by new operational conditions for electric trucks. By leveraging datadriven
methods instead of the truck’s physical properties, the thesis assesses the
characteristics and robustness of different machine learning models in predicting
energy consumption under various new conditions. The study focuses on scenarios
involving uni-, bi-, and multivariate covariate shifts posed by colder temperatures,
a new route type, and a new vehicle manufacturer. Utilizing real-world data from
electric truck drives, the models are trained on source data, adapted using limited
target domain data, and assessed for their probabilistic calibration in the target
domain. The findings indicate that out of the two baseline models, Ridge regression
models, the source-trained baseline model performs well under simpler shifts but
struggles with multivariate shifts where the target-only baseline model excels given
sufficient target domain data. Hierarchical Bayesian linear regression shows high
adaptability when covariate shift affects hierarchical levels of the model. Gaussian
process regression improves comparatively well with adaptation. However, the
results indicate a possible sensitivity to kernel selection. Bayesian neural networks
face challenges with prediction mean accuracy and high sensitivity to individual
samples, further research is needed to determine the model’s feasibility in a FSDA
setting. These insights provide valuable guidance for fleet management companies in
improving decision-making and operational efficiency under new driving conditions
through accurate probabilistic energy consumption modeling.
Beskrivning
Ämne/nyckelord
few-shot domain adaptation, probabilistic machine learning models, covariate shift, distribution shift, energy consumption prediction, probabilistic calibration, predictive distributions, electric trucks.