Result for 1FDDA4B4FED3C02A36245673B1715DD74DB55021

Query result

Key Value
FileName./usr/lib64/R/library/woeBinning/R/woeBinning.rdb
FileSize68854
MD5507265743B3325A42FE8433323DE00F2
SHA-11FDDA4B4FED3C02A36245673B1715DD74DB55021
SHA-2560CE504784FCA3E39EB01FFE3723C9F3D6363B1D88204015FB5AD6214325AD4A1
SSDEEP1536:zqB3XsjN5Y+U12szeoyvb9JXjq+ddVbjm4OMY3FIR2N7hdmYOGC3p4E7Kvl:1N5Y+U1/eoyxU+RYmR2NnmYZEed
TLSHT1256302D40BCC1A617CA19A5052235BB489FAC9F06BF748EE927CC7A039B457BA335CC5
hashlookup:parent-total1
hashlookup:trust55

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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
MD5C6B4C0CB6BD6A459E1450E00E6858922
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
PackageRelease2.19
PackageVersion0.1.6
SHA-1234CE22222244B1BB5C17533E33C0CD5D637286D
SHA-256A645BA65B107EF8209A95D21D0E1B460605A6A5449427D4947B220E852317CDD