Definition: "A.B.A." is an acronym used in the field of data analysis, particularly in the context of statistical models for binary classification (a type of machine learning). It stands for "Random Forest, Agglomerative Clustering, And Bayes' Tree". A Random Forest is a supervised learning algorithm that uses random sampling to split the training set into several subsets. The resulting subsets are then used as input features in the model and the model is trained using each subset one after another. The final output from the model is an output probability distribution over all possible outputs. The Agglomerative Clustering method is a topological clustering algorithm that iteratively partitions the data into clusters based on similarity between its points. It takes a binary classification problem as input, and produces a partition of the points with cluster centers forming a set of clusters. The resulting clusters are then used to produce predictions or decisions. The Bayes' Tree (BTree) is a type of decision tree that uses bayesian information criterion (BIC), which penalizes overfitting more heavily than other criteria in estimating models and improving their predictive accuracy. It was developed by John G. Smith, who presented the first paper about Bayesian networks.

A.B.A.