I have been trying to evaluate my models used on fire systems dataset with a huge imbalance in the dataset. Most models failed to predict any true positives correctly however naive Bayes managed to do that but with a very high rate of False Positive. I had run the experiments on both the confusion matrix and classification report for both can be seen below. The same dataset and train/test split was used with both of the datasets
Naive Bayes Confusion Matrix and Classification Report [[TN=732 FP=448] [FN=2 TP=15]] precision recall f1-score support 0 1.00 0.62 0.76 1180 1 0.03 0.88 0.06 17 accuracy 0.62 1197 macro avg 0.51 0.75 0.41 1197 weighted avg 0.98 0.62 0.75 1197 Logistic Regression Confusion Matrix and Classification Report [[TN=1180 FP=0] [FN=17 TP=0]] precision recall f1-score support 0 0.99 1.00 0.99 1180 1 0.00 0.00 0.00 17 accuracy 0.98 1197 macro avg 0.49 0.50 0.50 1197 weighted avg 0.97 0.99 0.98 1197
However I got the Kohen Kappa Coefficient for these models and I am quite confused on how to interpret the values. Please find values below
Logistic Regression=0.0 Naive Bayes=0.03
These values indicate very slight agreement. But why is the value of Naive Bayes slightly better than Logistic regression ?