Interpretable Machine Learning Approach to Predicting Electric Vehicle Buying Decisions
Bibliographic record
Abstract
To address the problem of climate change emissions from the transport sector, many countries are promoting electric vehicles (EVs). To support such efforts, it is essential to know what influences the choice of an EV over a traditional internal combustion engine vehicle (ICEV). To study this, a discrete choice experiment was developed, and 2,015 valid responses were gathered from Canadian adults with a driver’s license. In place of a more traditional analysis, a machine learning approach, XGBoost, was applied. However, two key issues were addressed with respect to its application. First, a practical question related to how best to split the training and testing data was examined. A new technique based on the Coyote optimization algorithm (COA) is developed that automatically determines the split that leads to the greatest prediction accuracy. The policy-relevant results of the analysis found that an individual’s Climate Change-Stage of Change (CC-SoC) and the price ratio of EVs to ICEVs are the most important direct influences. The interaction effect of the first two (CC-SoC and price ratio) is also influential. However, this leads to the second key issue: interpretability. Although high prediction accuracy (87.1%) was achieved, the black-box nature of the approach limits its policy relevance. As such, this research applied a technique, Accumulated Local Effects (ALE), that can determine the strength and direction of influence of the variable. This research demonstrates how machine learning can be applied to a policy-relevant question and provide information that is useful to policy decision makers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".