What are the limits of machine learning in error detection?

What are the limits of machine learning in error detection?

Machine learning can struggle with error detection in cases of insufficient data, bias, and complex patterns.

Can you explain how insufficient data affects error detection?

Insufficient data can lead to overfitting, where the model learns noise instead of the actual patterns, reducing accuracy.

What about bias in the data? How does that impact the results?

Bias in data can cause the model to make incorrect predictions, as it may not generalize well to unseen data.

Are there specific types of errors that machine learning is particularly bad at detecting?

Yes, machine learning often struggles with rare events or anomalies that weren't well-represented in the training data.