Types of Noise in Machine Learning
Following are the types of noises in machine learning-
- Feature Noise: It refers to superfluous or irrelevant features present in the dataset that might cause confusion and impede the process of learning.
- Systematic Noise: Recurring biases or mistakes in measuring or data collection procedures that cause data to be biased or incorrect.
- Random Noise: Unpredictable fluctuations in data brought on by variables such as measurement errors or ambient circumstances.
- Background noise: It is the information in the data that is unnecessary or irrelevant and could distract the model from the learning job.
How to handle Noise in Machine learning?
Random or irrelevant data that intervene in learning’s is termed as noise.