What is noise?
In Machine Learning, random or irrelevant data can result in unpredictable situations that are different from what we expected, which is known as noise.
It results from inaccurate measurements, inaccurate data collection, or irrelevant information. Similar to how background noise can mask speech, noise can also mask relationships and patterns in data. Handling noise is essential to precise modeling and forecasting. Its effects are lessened by methods including feature selection, data cleansing, and strong algorithms. In the end, noise reduction improves machine learning models’ efficacy.
How to handle Noise in Machine learning?
Random or irrelevant data that intervene in learning’s is termed as noise.