What is the impact of noise in machine learning data?

What is the impact of noise in machine learning data?

Noise in machine learning data can lead to inaccurate models, as it introduces random variations that obscure the true patterns.

How does noise affect model training?

Noise can cause overfitting, where the model learns the noise instead of the underlying data distribution, reducing its generalization.

Can we reduce noise in our datasets?

Yes, techniques like data cleaning, outlier detection, and using robust algorithms can help mitigate the effects of noise.

What are some examples of noise in data?

Examples include measurement errors, irrelevant features, and random fluctuations in data collection.