TreeGen -- a Monte Carlo generator for data frames
This work provides a new data structure and generation method for data scientists working with data frames, offering potential benefits in data multiplicity, compression, and hierarchical modeling.
This paper introduces the probability tree, an extension of decision trees, to encode occurrence frequencies and relations within data frame rows. It is accompanied by a Monte Carlo generator module, implemented in the TreeGen Python package, for various data science tasks.
The typical problem in Data Science is creating a structure that encodes the occurrence frequency of unique elements in rows and relations between different rows of a data frame. We present the probability tree abstract data structure, an extension of the decision tree, that facilitates more than two choices with assigned probabilities. Such a tree represents statistical relations between different rows of the data frame. The Probability Tree algorithmic structure is supplied with the Generator module that is a Monte Carlo generator that traverses through the tree. These two components are implemented in TreeGen Python package. The package can be used in increasing data multiplicity, compressing data preserving its statistical information, constructing hierarchical models, exploring data, and in feature extraction.