How can we reduce false negatives in AI models?

How can we reduce false negatives in AI models?

Reducing false negatives involves improving model accuracy. Techniques include better data quality, feature selection, and tuning thresholds.

What role does data quality play in this?

Data quality is crucial; high-quality, representative data helps the model learn better patterns, reducing false negatives.

Can you explain feature selection?

Feature selection involves choosing the most relevant features for the model, which can enhance its ability to identify true positives.

What are some common techniques for tuning thresholds?

Common techniques include ROC curve analysis, precision-recall trade-offs, and cross-validation to find optimal thresholds.

Are there any tools that can help with this process?

Yes, tools like Scikit-learn, TensorFlow, and Keras offer functionalities for model evaluation and threshold tuning.