Nested Logit Models and Artificial Neural Networks for Predicting Household Automobile Choices: Comparison of Performance
Why this work is in the frame
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Bibliographic record
Abstract
Over the past few years, machine-learning techniques have expanded enormously. These approaches are increasingly being applied to traffic and transportation problems formerly reserved for formal statistical approaches such as discrete choice models. Part of the reason for this has to do with research trends, but there are some potential advantages associated with such techniques, including the ability to model nonlinear systems; the ease with which symbolic, nominal, or categorical variables can be included; and the ability of these methods to deal with noisy data. The use of two modeling techniques, the nested logit model and the multilayer perceptron artificial neural network, was investigated in terms of their applicability to the household vehicle choice problem. Both methods generated strong results, although the multilayer perceptron artificial neural network yielded better predictive potential.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it