Real-world data, which is used as an input for data mining algorithms, can be affected by several variables. Noise is one of the most common challenges faced by organizations that focus heavily on data-driven solutions. When it comes to machine learning, Noise refers to any errors or inconsistencies in the training data. Noise can be present in the form of missing values, outliers, data entry errors, duplicate data entry, etc. Noise reduction is a crucial requirement since machine learning algorithms may view this noise as recognizable patterns and potentially begin generalizing incorrect assumptions from it.
Applications of Noise
Regularization
Using noise in machine learning models as a regularization technique can help to prevent overfitting on training data. By smoothing out the variations in the data, noise regularization can reduce the complexity of the model and improve generalization to new data.
Data augmentation
Adding noise to the training data samples can increase the sample size and diversity, allowing the model to learn a more robust representation of the input data.
Generative adversarial networks
Generative models like GANs use noise to generate diverse samples similar to the original data.
Reinforcement learning
In reinforcement learning, adding noise to the action taken by an agent can encourage exploration of the environment. This may lead to improved learning performance.
Related terms
Data augmentation Generative adversarial networks Machine learning Model
Training data Generalization
