Result for 5EEAD092155F1D7DA5434CB586DDD1A773ECD99D

Query result

Key	Value
MD5	767A4326CEC11A15EA719F7813EFC50E
PackageArch	aarch64
PackageDescription	Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world, building upon numpy, scipy, and matplotlib. As a machine-learning module, it provides versatile tools for data mining and analysis in any field of science and engineering. It strives to be simple and efficient, accessible to everybody, and reusable in various contexts.
PackageMaintainer	Fedora Project
PackageName	python-scikit-learn
PackageRelease	1.fc21
PackageVersion	0.15.2
SHA-1	5EEAD092155F1D7DA5434CB586DDD1A773ECD99D
SHA-256	9C004EAB2833A4A0DD95A2ACF788B49B1817B534C5AB272F88DBF4A00194614B
hashlookup:children-total	906
hashlookup:trust	50

Network graph view

Children (Total: 906)

The searched file hash includes 906 children files known and seen by metalookup. A sample is included below:

Key	Value
FileName	./usr/lib64/python3.4/site-packages/sklearn/ensemble/tests/test_forest.py
FileSize	17601
MD5	856C9EB5C6357A8E978BB27ED478B14D
SHA-1	0001980ED073FFA12C4D97EE33F9FC4D4A9FF043
SHA-256	95CDF4DE2328FC18906E92054FE52629B8B6B99CEF8992750C9EA14D9533FA72
SSDEEP	384:RmH3A2etKtuw8ixVT17yl8iMX5nITJojVpKv+66wLoVE/wpL+:RmH3A/tKtuw9xVT17yl8NX5nITJojVpu
TLSH	T18482D703F8960D595B53297E24DE510827956B1B860818753EFFD0086F9462CB3FBBBE

Key	Value
FileName	./usr/lib64/python3.4/site-packages/sklearn/linear_model/ransac.py
FileSize	13952
MD5	67B38A5B19534C15626BEDDA27CE70D0
SHA-1	000C0BD44626C1E94A98B9CB8615101BC35C180F
SHA-256	526176561F560882ECAE0B67F451191EBFC36F7E9228B327F61452F553983492
SSDEEP	192:1axKzOGnKFnGWjPfIAeMl0Bgox2WGZHO6qWKNRKES6dIhBNRERZCoNbk7A:oK68KRTfIAXkaHRKS6aBsRZhb7
TLSH	T17F52940568203B374A87B5B068DE010BC77918A79686A4757CFCC3AD1F6297873ADBD8

Key	Value
FileName	./usr/lib64/python2.7/site-packages/sklearn/ensemble/tests/test_partial_dependence.pyc
FileSize	7056
MD5	1A392AB491B0876F61650F6038170E8D
SHA-1	002419306404806E3DD60025F92C41987DADB3FE
SHA-256	49EC3366B520F9E68531FD88842E4A49EB47041ED83138FB4DB1E283A0A57432
SSDEEP	192:FBa4Tp2QraMiqLjjCHg3jC3+U2uAmII6C3f3SKJD888pC:F7kwuEIj888c
TLSH	T141E12142A3D5DB7BC1B212BAB8F4421BEDA9F46B2B892700517CC43E39D8755C12F784

Key	Value
FileName	./usr/lib64/python2.7/site-packages/sklearn/ensemble/tests/test_bagging.pyo
FileSize	14670
MD5	D9A2E5D7AC76D27E9D0CFC161E667681
SHA-1	003A8029AC9FA95074A0569187F6A63E82F410C6
SHA-256	3E2EB2CEB84175C775BB431A2EA8D157EA429F988C8F88C0BC538EA83F0806D1
SSDEEP	384:aJY+KxN7fKOPeKxKjv8zh9mDUNciTtCtsMthth8888T:YY+Kx9yOZxKAIDiciTtCtsMthtp
TLSH	T1F2625081E3E18B9BD53113B59CF0125BAEA1F87BA105772502BCF87D3AD8724D22B385

Key	Value
FileName	./usr/lib/python3/dist-packages/sklearn/utils/sparsetools/_graph_validation.py
FileSize	2407
MD5	6CCA3A2DFA57FF6AF3CF3A27AE22F209
SHA-1	01070C25205C477A297A7CCE48DA78871F64DD2C
SHA-256	298C9425EE8888DD03C6A32021051C1ACE1D8C45775B277F0095589690515DD8
SSDEEP	48:PLdf167rziXSwtpF8AyEv9iVfkZY2MiV8K2pq:DL6fep8AJYVfkZLFKtpq
TLSH	T1FE41FE25932D0564D16380E48C83A70E1AD8F6073F67242DF4EEBC682F3861C63257BD

Key	Value
FileName	./usr/share/doc/python-sklearn-doc/stable/_downloads/plot_polynomial_interpolation.py
FileSize	1895
MD5	A4CC2943F64D2730EF80B9504C583D19
SHA-1	011BDEF5443BE65B5EC29C9D37FCEEC7206429FA
SHA-256	2B12D9E9919C21B4BFF58007AB9F645B717AE7749E79099AFBB8B253B5A3ABFC
SSDEEP	48:3b/2fr4glFa11YCuArC18AlcCxaD+1sozVGsA9MGNr:z0lAO18gcCE+BgPr
TLSH	T15541B9092E55E82107364074B6F898616E19046EAE8305663DCDBE301B42B0F3D3BF47

Key	Value
FileName	./usr/share/doc/python3-scikit-learn/examples/cluster/plot_cluster_comparison.py
FileSize	4865
MD5	919283D95801BDB1582E6768ADC62A65
SHA-1	0145D31CB950A8CC679300AF4CF93EC48DE5D612
SHA-256	64C861D3DC5FE9F11F44F2AA5A86FF15BEB60B26B7974895663F37DC729039C3
SSDEEP	96:hLrD8Hd/MIsALpqtjAFejIHXSNIuGytASwTgSNexmDDz4bW:h4nVBgZ/6tLQW
TLSH	T176A1857167126117EF93B09A4EB751E837946057075028AAB52CC3254F0BB3CB3F2B9B

Key	Value
FileName	./usr/share/doc/python3-scikit-learn/examples/exercises/plot_cv_digits.py
FileSize	1207
MD5	C21A69A2BC54F263E69035C048095865
SHA-1	016D65381370139D98DCC375AACCF083CD195B82
SHA-256	657225AA5357703DBAC9E250E5690997774CA4C566BC32D257203E93FCAB5E17
SSDEEP	24:akV7BmSxOgUWqNqag5YEA5BRklGiVQ+zAsyPs1J:akaSVUNJEMBRkbuWyOJ
TLSH	T1B621DC0CBAA6B2780B9284B4FC44507137E393106708683E78ABDC6D5646F372B61CB2

Key	Value
FileName	./usr/share/doc/python-sklearn-doc/stable/_downloads/plot_swissroll.py
FileSize	1446
MD5	6C764C92907310B7717595E840304798
SHA-1	0193A74906128D26183FB66001B61CA5D447B865
SHA-256	5D7791C51D76DD46308EE5B4B799509831C1EEDC2D767C39B78E7E98A39B066D
SSDEEP	24:x2RAnm7PXQ2KQsFe3M/MDyC5NJYTC4aeujm5tSU3+LVJU3+PbYY1+BZjs:xEAn12KED3JLe0atSfJNYYqs
TLSH	T116313F1C2E07B27697A2F0E83E6417DDEB515A009F2044F8B83D68F45381B7CB82D51B

Key	Value
FileName	./usr/lib64/python2.7/site-packages/sklearn/datasets/mlcomp.pyo
FileSize	3461
MD5	D9C824CEEC69C260CA1042D388AF7600
SHA-1	020EBAE10920869646ADDF003F4E65839F8AFB3A
SHA-256	B241A985AFAC860EC24C48716E78D5865CCFD9F3E59FE8997D8740140A3AACE6
SSDEEP	96:7Ds7QEVR5R3OLtHcODmW/FuskVRVz5CwoVM3gZJVEjlnfkC0hpl:385zSHcODHFuskRz5InEjRfkCkl
TLSH	T1B7613481BB90C6779460547472B84113CA61F6FF624067913AECE0B81FACBA1A0BF7A1