Introduction
The application of Artificial Neural Networks (ANNs) in understanding complex societal issues has been gaining momentum. One such challenging area is the analysis of fatal police shootings. By employing ANNs, we aim to unravel the intricate patterns that could explain the dynamics behind these critical incidents.
Methodology
Our study utilizes a robust dataset encompassing various attributes of fatal police shooting incidents, such as demographics, the armed status of the individual, and whether signs of mental illness were evident. We trained an ANN with a single hidden layer of 50 neurons, using a split of 80% training data and 20% test data.
Results
The ANN model’s performance on the test set yielded an accuracy of 67%, showing promise in its predictive capabilities. However, it’s clear that the model has a stronger grasp on incidents classified as ‘Attack’, as opposed to ‘Other’ threat levels:
- ‘Other’ Threat Levels:
- Precision: 57%
- Recall: 44%
- F1-Score: 50%
- ‘Attack’ Threat Levels:
- Precision: 71%
- Recall: 80%
- F1-Score: 75%
These results suggest that while the ANN can reasonably identify ‘Attack’ incidents, there’s substantial room for improvement, particularly in correctly classifying ‘Other’ threat levels.
Analysis
The disparity in the model’s ability to predict different threat levels points to potential areas of focus for future model refinement. The lower precision and recall for ‘Other’ threat levels indicate the model’s difficulty in generalizing the characteristics that distinguish these incidents.
Recommendations for Improvement
- Network Architecture: Experimenting with additional hidden layers or adjusting the number of neurons could enhance the model’s learning capacity.
- Class Imbalance: Strategies such as oversampling the minority class could help the ANN learn more about underrepresented patterns.
- Hyperparameter Tuning: A systematic search for the best hyperparameters may optimize the model’s performance.
- Feature Engineering: Improved feature selection could reduce noise and focus the ANN’s learning on the most predictive attributes.
- Model Interpretation: Tools that illuminate the ANN’s decision-making could provide actionable insights and bolster trust in its predictions.
