Dangerous Prediction in Roads by Using Machine Learning Models
Bibliographic record
Abstract
Many vulnerable, heinous acts that are coming about in the society especially at Roads, most specifically affecting women in the society, are more in recent days. Though new technologies are developing day by day, the fatality rate is not in control to date. Without proper guidance to the people about the particular place where there is a big scope of occurrence of a greater number of accidents, this menace cannot be regulated. It is required to highlight the District-wise data and Roads where the accidents and fatalities are more. The data would help the policymakers to put in place Focused Initiatives regarding those top dangerous roads to address the menace of rising road accidents and resultant fatalities. In this, we created a dataset in Andhra Pradesh where we include those attributes that are helpful for our analysis to predict which road is the most dangerous one. We applied various Machine Learning models such as Logistic regression, Random forest classifier, Gradient Boosting Classifier, Gaussian Naive Bayes, Decision Tree Classifier, K- Nearest Neighbour Classifier and SVM to predict the dangerous roads. It is observed that Logistic Regression provides good accuracy with 87.14.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".