Today, I conducted a logistic regression analysis to predict flee status, employing features such as age, gender (male/female), vehicle presence, armed or unarmed status, and race (black/white). The target variable was ‘flee_status_not,’ where 1 indicates did not flee, and 0 indicates fleeing. Here are the key observations from the results:
- Accuracy: The model demonstrated an accuracy of approximately 70%, signifying its ability to correctly predict ‘flee_status_not’ for 70% of the samples in the test set.
- Precision: Of the instances predicted as ‘flee_status_not,’ 72% were genuinely ‘flee_status_not,’ implying that 72% of individuals did not attempt to flee.
- Recall: Out of all the actual ‘flee_status_not’ instances, the model correctly identified 94%.
- F1-score: The F1-score, representing the harmonic mean of precision and recall, is a useful metric for balancing these two measures. With an F1-score of 0.81, the model demonstrates a reasonably good balance between precision and recall for predicting the ‘flee_status_not’ class.