How can I reduce false positives in AI systems?

What are some common causes of false positives in AI?

Common causes include biased training data, poor feature selection, and inadequate model evaluation.

How can I improve my training data to reduce these false positives?

You can improve your training data by ensuring it's diverse, representative, and free from biases.

Are there specific algorithms that are better at minimizing false positives?

Yes, algorithms like Random Forest and Gradient Boosting often perform better in reducing false positives.

What evaluation metrics should I use to measure false positives?

You should consider metrics like Precision, F1 Score, and ROC-AUC to evaluate false positives effectively.