My KNN model have 80% accuracy and 85% precision and SVC have 79% accuracy and 85% precision. What is the reason behind this? or both of these models are stable or not to use?
It is a nice question. With regard to this question asked, you need to understand the theoretical as well as physical interpretation of confusion matrix. Additionally, you need to make out the meanings of accuracy and precision, as well. Then only, you will be able to comprehend the same.
It is a nice question. With regard to this question asked, you need to understand the theoretical as well as physical interpretation of confusion matrix. Additionally, you need to make out the meanings of accuracy and precision, as well. Then only, you will be able to comprehend the same.
Precision only accounts for true positives and false positives. You likely have more false negatives than false positives, which affects Accuracy more than Precision. You should always use precision along with Recall in order to confirm such issues, or even a combined measure such as the F1-score.