Causes of Noise
- Errors in data collection, such as malfunctioning sensors or human error during data entry, can introduce noise into machine learning.
- Noise can also be introduced by measurement mistakes, such as inaccurate instruments or environmental conditions.
- Another form of noise in data is inherent variability resulting from either natural fluctuations or unforeseen events.
- If data pretreatment operations like normalization or transformation are not done appropriately, they may unintentionally add noise.
- Inaccurate data point labeling or annotation can introduce noise and affect the learning process.
How to handle Noise in Machine learning?
Random or irrelevant data that intervene in learning’s is termed as noise.