| Key | Value |
|---|---|
| MD5 | 1D0C6AB50EBD1BF6667D3261B5645A4E |
| PackageArch | ppc64 |
| 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 | 0169901D8BFCBF3C6BF66F677D486CB311B6F443 |
| SHA-256 | 965CEC779FC6E5074A2AC7165B26BA28D0778BD2CB53441F1380C3C8BD0B169B |
| 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/lib64/python2.7/site-packages/sklearn/neighbors/kde.pyo |
| FileSize | 7302 |
| MD5 | 95483C151074B04138773447AFE8FAB7 |
| SHA-1 | 00CB86A178DAC295EEE19F82AB932EB7C538753B |
| SHA-256 | 07780C6EAB2A62D391D002E6800783ADE8086918489381083F21E588346BD4F2 |
| SSDEEP | 192:5XAe9KWiYrMZ9n0GGQuIy7zctZ9khzrBAyq1iS+n:F6469naI9UAdo |
| TLSH | T1E6E1A505AE918737D8E2C572A8F0000BDB39FD3BBA4127113ADC95352FD5A75D26E389 |
| Key | Value |
|---|---|
| FileName | ./usr/lib64/python2.7/site-packages/sklearn/utils/fixes.pyo |
| FileSize | 7847 |
| MD5 | 3AA14AFD03C857AEA5D0F093F2248D7B |
| SHA-1 | 00E579BB3FFC5FA63633CDF9E4F4EB768501C9D9 |
| SHA-256 | 201601DFD2409F273A5D1C149422E42FD01EA5C0E387288B840DF1C961027236 |
| SSDEEP | 192:fkVotcydhLULpGrjXC2TKbKF0hLsgdQ31cUWT6h8JjFFm:fH15K2Y9hNCFc9H5O |
| TLSH | T107F17284F7E15A6BD5B4427951B002179FF5F1B7A201275022F8E47E3EDC7A6C23E298 |
| 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/lib64/python2.7/site-packages/sklearn/tests/test_pipeline.pyo |
| FileSize | 10966 |
| MD5 | D1B34C45F5EA1968D5CB75D77F63331D |
| SHA-1 | 01C85BD3240ECBBBC7DD85F97A3B5CCAB4F3559B |
| SHA-256 | 8D072235774902D8B8FCA17A119E54C06F60A1CBA49AD1C0B8ECA4BB187BA9E3 |
| SSDEEP | 192:XzFZNxiGqaVmerIU+gwtuNrFVMgOFRP9+Q5xl0qS1bZ8+oeK9I7SvM+OHYCtjN8m:jvN9UuA3PvEiqG+oVvLUJN88z |
| TLSH | T14F322084A7E64BD7D070267590F0431BADA5F977A1403B4163BCE83A3DD836AD91B3CA |
| 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/lib64/python2.7/site-packages/sklearn/linear_model/tests/test_sparse_coordinate_descent.pyo |
| FileSize | 8736 |
| MD5 | E1299F9472CFC03764B0B26D3EDFAF15 |
| SHA-1 | 02D4BA301D1C0BC4AEB6FDBA64F6E7BAE7C84824 |
| SHA-256 | 343FDE0A99A7BC0D6FC6EBC04B789962329FA78F4614812869C553A781E9C2A7 |
| SSDEEP | 192:aZoxf7+fVTvl8fY9kcTfIEDBZvfiYL0EfNxyv4t6FfqkZ0fszHqf1pfjoxbfHIXE:b7CVTuY9xIEDBxiS08NxsJwU2jjoJHY8 |
| TLSH | T15D02CC41A3DA8E9BC1741035A4F00703ADA4F1FABE45AB4215ECE87D7ED43A5C56F38A |
| 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 |
| Key | Value |
|---|---|
| FileName | ./usr/share/doc/python3-scikit-learn/examples/ensemble/plot_adaboost_multiclass.py |
| FileSize | 3585 |
| MD5 | 34017803563CB9F9B635C002523C75AB |
| SHA-1 | 0322042E7BF79C438AD29D097C0E77A689587F33 |
| SHA-256 | 50E1E9107975866013E5FFB4FA9033867E9BB126CC32959A3E9CC4E3E4204164 |
| SSDEEP | 96:ir9H8frWUXgINfBhroMYbPXwW/K/oKR6m257TwDLj5dlTn2093:DC3iXR6mp1N |
| TLSH | T1F07193258A666A3187B96CFECCAC526D3360144C9D22D009B5FD8F300F0BF19ECBA2D4 |