semvec/pypi-packages huggingface dataset:
coarseclassevaluator 1.1
CoarseClassvisual formulator
Requires Python: >=3.6

# Project Discription

### CoarseClassvisual:
This package helps a Data scientist to reduce the number of levels inside the category to <=5 levels for a binary classification problems, 
This package is the implementation of WOE based binning, and uses similar WOE to collate bin.

Below are the applications of this package

1. Explicability: Improves explicability of the story teeling of patterns. 
Instead of saying NY, SF, Seattle has more loan defaults this can logically group cities using this package and say all the hi-tech & Financial sectors are has more loan defaults
2. Reduces Noise and sparsity: With too many levels of predictor of binary classification , It becomes too noisy and upon dummy variables increases sparsity. This package helps
one reduce the Noise and sparsity
TripletVoting 1.2
The tripvote Python software
Requires Python: >=3

This is an implementation of tripVote: complete a set of gene trees to maximize the quartet score. The core algorithm is the Maximum Triplet Rooting (MTR) problem: Root a tree T according to a reference tree R such that the triplet score of T and R is maximized. The algorithm and the code heavily depend on the tqDist software http://birc.au.dk/software/tqDist/. We would like to thank the authors for the code and algorithmic ideas.