Predicting Crime Rate in Toronto
Bibliographic record
Abstract
Predicting rates of criminal activity is a critical domain of study that has captured substantial interest in contemporary times. Various statistical, deep learning as well as machine learning techniques are being used to predict occurrence of crimes based on different factors such as time, location, and socioeconomic conditions. The use of big data analytics and predictive models are capable of assisting law agencies by taking premptive actions to avoid crimes and improve the safety of public. Several studies have shown that predictive models based on algorithms of machine learning like Decision tree, Neural Networks, and Random forest can provide accurate crime rate predictions. The efficiency of such models is mainly reliant on the quality of the information and the features used in analysis. In this project, A Random Forest classifier is used to create a classification model to predict the kind of major crime that has taken place based on various factors along with neighbourhood, division, time of crime, month, year and so on. In Toronto between 2014-2019, a detailed information including where and when the crime is committed and various other factors are included in our dataset, containing categorical data. The process involves in testing the data using both OneHot and numeric encoding. Despite the uneven distribution of instances, where assault records are in the majority, the model delivers satisfactory results on F1-score when dealing with the five class classification problem.Model’s performance doesn’t get affected while balancing the class weights. The model that uses Random Forest Classifier and One Hot Encoder shows a slight increase in accuracy, as indicated by an F1-score.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".