Adaptive boosting, also known as AdaBoost, is a machine learning algorithm used to improve the accuracy of a classification model. It works by iteratively training a series of weak classifiers on a dataset, and then combining their predictions to produce a more accurate final prediction.
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.
Ad Hoc Analysis is an exploratory analysis technique used to uncover patterns or relationships in a dataset. Often times this work is performed “Ad Hoc” or “On The Fly”… meaning it it done in the moment.
Active Learning is a type of machine learning where the algorithm actively chooses which data points to use in order to learn from.
Absolute Error is the difference between the predicted value and the actual value.