Noise
Noise refers to irrelevant, random, or inaccurate information in data that can interfere with a machine learning model's ability to identify meaningful patterns and make reliable predictions.
What is Noise?
Noise can occur in training data due to measurement errors, incorrect labels, missing information, corrupted inputs, or natural variations that are unrelated to the target outcome. If excessive noise is present, a model may learn patterns that do not generalize well to new data.
Why is Noise Important?
Reducing unnecessary noise can improve data quality and help models learn more meaningful patterns. However, not all variation is harmful, so removing useful information along with noise can negatively affect model performance.
Common use cases
Noise is commonly addressed during data cleaning and preprocessing for image recognition, speech processing, sensor data, natural language processing, time-series forecasting, and other machine learning tasks.