Interpretable Machine Learning Approaches to Prediction of Chronic\n Homelessness
Bibliographic record
Abstract
We introduce a machine learning approach to predict chronic homelessness from\nde-identified client shelter records drawn from a commonly used Canadian\nhomelessness management information system. Using a 30-day time step, a dataset\nfor 6521 individuals was generated. Our model, HIFIS-RNN-MLP, incorporates both\nstatic and dynamic features of a client's history to forecast chronic\nhomelessness 6 months into the client's future. The training method was\nfine-tuned to achieve a high F1-score, giving a desired balance between high\nrecall and precision. Mean recall and precision across 10-fold cross validation\nwere 0.921 and 0.651 respectively. An interpretability method was applied to\nexplain individual predictions and gain insight into the overall factors\ncontributing to chronic homelessness among the population studied. The model\nachieves state-of-the-art performance and improved stakeholder trust of what is\nusually a "black box" neural network model through interpretable AI.\n
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".