Additive Smoothing

Definition of Additive Smoothing

Additive smoothing is a technique used in data science to smooth out noisy datasets. It is a form of noise reduction, and it works by adding a small amount of noise to the dataset in order to obscure the original noise. This makes it easier to identify the underlying trends in the data.

What is Additive Smoothing used for?

Additive Smoothing (also known as Laplace Smoothing) is a technique used in natural language processing and machine learning for smoothing categorical data. The idea behind this technique is to add a small amount of probability to each outcome that occurs in the dataset. This prevents zero probabilities from occurring, which can lead to erroneous results during inference or prediction. It also helps reduce noise and over-fitting since it puts more emphasis on the existing data than any new data points that might be added during testing or prediction. Additive smoothing works by adding a small constant k to all values within a dataset, effectively changing the distribution of probabilities across outcomes. For example, if there are two outcomes A and B with initial frequencies of 10 and 8 respectively, then after additive smoothing is applied the new frequencies will become 11 and 9 respectively. By having more equal probabilities for all outcomes, additive smoothing helps improve model performance as well as reduce over-fitting by making sure that no single outcome has an overwhelming advantage over the others when predictions are being made. Additionally, it can help prevent errors from happening in cases where an unseen value appears during testing or prediction, since there will still be some probability assigned to it rather than returning a result of zero probability.