Result for 2F78450A3661AC4197F98D8C48C9191B3CD1CD32

Query result

Key Value
FileName./usr/lib64/R/library/woeBinning/Meta/package.rds
FileSize974
MD57EFF88AFD0C0561CB2BB7571054C9D91
SHA-12F78450A3661AC4197F98D8C48C9191B3CD1CD32
SHA-256D29205EB0260B74BC1C15DF5320DFDA02862CF8B647B30C0EF9487716213E3F5
SSDEEP24:XWvxtLSPFH3MtkDt54hYYbOUBTZd0vFKxmZ17GOTFI3gz4K:XPPNd2hYDM1d0vF7f7G0O3k9
TLSHT1C711C896341F4D07EFE3E67686DD9C0A720F816D214528F509350DD3D88DE991B2BB83
hashlookup:parent-total1
hashlookup:trust55

Network graph view

Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5DEDD7A3838CB9ADFCD7C692A3646E9B2
PackageArchx86_64
PackageDescriptionImplements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data.
PackageNameR-woeBinning
PackageReleaselp153.2.3
PackageVersion0.1.6
SHA-19115279CC30FADCD84855A908603CE95447EBD1E
SHA-25692DC3C42D4D5995022D44CCEC92358AE2998F53C2818058875F09E0E7DD2022F