| Key | Value |
|---|---|
| MD5 | 1FD1DE5E580AF24AE54C4277A040DED4 |
| PackageArch | ppc |
| 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 | 3.fc20 |
| PackageVersion | 0.14.1 |
| SHA-1 | EAD4EA1615DA0A87F1AFA8696B3E8010F934DCC8 |
| SHA-256 | 5E29271489BAE4D4A254B276427C8902D05CC838070D4A403FE1DB3BA5DB4910 |
| hashlookup:children-total | 801 |
| hashlookup:trust | 50 |
The searched file hash includes 801 children files known and seen by metalookup. A sample is included below:
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-sklearn-doc/examples/linear_model/plot_ols_ridge_variance.py |
| FileSize | 2032 |
| MD5 | 44A08C8EAB78FA8522617D143280CE43 |
| SHA-1 | 002D35237E1045669AE711D219C5C7E2C828DF64 |
| SHA-256 | DF23A76B9DA4857633A934C3CDEAC1FB3F1C3C0BFA5266F2CF148F22CC3F7240 |
| SSDEEP | 48:4YOa+3VOPSAN5mAOxGwq0JcrsyPAhpa2b7TyJANfeK6AK/l:4YOTlvsmAOxGwvJdyP7ifmWeK6Ail |
| TLSH | T15841861B62861B73A337942DBDB9329C7351409F79427CA57BFC61085F8172C0EB94B5 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.7/site-packages/sklearn/utils/tests/test_murmurhash.pyo |
| FileSize | 3483 |
| MD5 | DB91539DDB6FDB37BB2343C701D16BE5 |
| SHA-1 | 006FE3CBC319E8F53F1DC17D70D53A2FAE5F16F5 |
| SHA-256 | 83BE022D0095FB31CEB41EFC94151051431408E9D7486F29C2D79015AAB21A9D |
| SSDEEP | 48:5rkVw0le5CVVTJ0mDYrfTLTKCHv507jl0xj60u+lqruQwk0YchzETQaYacE0Z+UL:N0EUlimDYrOuq7Cx5J95ZZ+U8n4f |
| TLSH | T16671BD40A7F70D9BD2A4107EB570031BEEE5F4B33204639212B4A43E26DC76E996E7C6 |
| 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/examples/exercises/plot_iris_exercise.py |
| FileSize | 1577 |
| MD5 | 3CEE1240FBA2960897069B76B5637772 |
| SHA-1 | 0116272B06B5037C5FC6E48E289CAD5FC1E6CC61 |
| SHA-256 | BE809F9603D572D38F9B2B5C30FDDBC3865711F28480FD46C2EDFED3DB78BC83 |
| SSDEEP | 24:/AX9SV6wq4Vxknvg059WbkKX52BrpH2sCU5tkLtmGvItyG4bxpZNbH/D:IXcVVVqrKX5m8lMtggGPbxfNz/D |
| TLSH | T1F031201A904E337213C790BD82EB29846B5366234B44687A777DB7D1DF02764F239942 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.7/site-packages/sklearn/preprocessing/_weights.pyo |
| FileSize | 1191 |
| MD5 | BFD9DF60E19D9971C796589E49058B66 |
| SHA-1 | 011F6CA18F885446DCC7645939E2C53C9D4475E2 |
| SHA-256 | 8DC14EAD86FB86813E9FDC82CB6849E8DAECEA466F87BE74E5E5432FB412C777 |
| SSDEEP | 24:FrXSxgNrZPI9llZlDJ+2VzWvt90WMrddNUUS40WXhi70Wd+:FrXSOxZP8llZ7+2Vavv07rddNtS40Uiu |
| TLSH | T1A721CB74B7901E87D1A096736090122BDB99E4337361A61236FCF4362F8D395622F689 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.7/site-packages/sklearn/datasets/tests/test_lfw.pyo |
| FileSize | 6207 |
| MD5 | 6D6C3F8A2EAEEB41D63ACCD27B73B0F6 |
| SHA-1 | 01290697FA448DBBE0ADC1A1A658610165854000 |
| SHA-256 | F34625DFEA8014D09E70C480D22E424E7296F0587016E8D740472F5BE0FF3A5B |
| SSDEEP | 192:xbr0EInYlqsLzsaAm0JhL2KEEsS+Q9X5j5bl:ttkM4s0JYS+2l |
| TLSH | T1CCD1558167E6CA27E2E05135A1B01303EE73F2B7AD10534216FCF8762DCA369D56D2C6 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.7/site-packages/sklearn/datasets/tests/test_covtype.pyo |
| FileSize | 1355 |
| MD5 | 56D9B857F9C98E109F0A709537D35D81 |
| SHA-1 | 02375F0A1F3B38F31BEA59FFAB4117A20C8FD8C5 |
| SHA-256 | 606E272DA80923714021741CA5ACCF1ED304827E76644E08D821AB672C89A615 |
| SSDEEP | 24:EPBzjQVof5A8ZH+0HJ2kRsQDZ8iEi394q/lln0HJyCse/0HJuqMQ:4BgVoRAB0pnsQDqi+q/lln0pyCsu0pj |
| TLSH | T1C42123C5B7FE8243D570A67C93748307BEB1F077960053501668B87A39D8315D0AF346 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.7/site-packages/sklearn/linear_model/ridge.pyo |
| FileSize | 36410 |
| MD5 | 2EDB4C9B35AA7F0C4A06EBA4292D1E72 |
| SHA-1 | 0299B6E791F6C71ECB2B100BDD8D89DC2AC3FF82 |
| SHA-256 | 768755C8C17FD2F35D7197B96314EBE73BEAD0B3CDF71428A09EEC4D7A294477 |
| SSDEEP | 768:Uap9lTrOaQwIyGC6+6Ugt9F31fV78kQ6M:PFrOaQ1yGCHzgtL31fVQkQp |
| TLSH | T1D9F28250BBAA0AABC522817674F402879BB4F07BD64237407AEDE1393FD4679C16F784 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-sklearn-doc/examples/plot_rfe_digits.py |
| FileSize | 852 |
| MD5 | 9103650C2F36397AB88DF835B34D38B5 |
| SHA-1 | 02C0A867F5D8E9C34607BCE93EF5ABF7C6F495A9 |
| SHA-256 | 9F282DADA6645094618C0A5BFA1913CC17F1384A2BBEC0B7B7783AB89CBE9928 |
| SSDEEP | 12:ilgJr4Y/8OREMeyAMyfbyvmvA9NZfXALqSkAVL/KO5Oe4N6sYD:iqh4E8HyLyTyOEfXALCAVIGh |
| TLSH | T141011E5D5220B7771DB758B582F5809319F20D3A2341622015A8CA658B82BB6FFF7A43 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python-sklearn-doc/examples/plot_kernel_approximation.py |
| FileSize | 7974 |
| MD5 | 92E29404D8F074E46F35C40F20766AB8 |
| SHA-1 | 02FB35C9AF06F6F6872DFFCAAC0A51570D42B6A2 |
| SHA-256 | BF5D973C48EC576C568FF9ECFB74EA75395EBF29B4D1C19AB82D210B6A4DDCF4 |
| SSDEEP | 96:0X75FrUiJKIiRjQBdLdgooMgFYZygEqTgR/KYIOcITTmpsm51iuPztYi:+75FQiti6KYZkR/KYIOcITq1iuhYi |
| TLSH | T17CF1E80B20E30B3223B7207C23DC21C7BFA49056E9975A3DF99D8654379AF21E276649 |