| Key | Value |
|---|---|
| MD5 | 909F587FEC3FD02B356EF44070ED72BD |
| PackageArch | s390 |
| 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 | F8719B4FACE61FACAB36FC9BA6F596AAD1298B07 |
| SHA-256 | 2292809F7EF68B3A84AE9A620ADDEC44404DF88E7F56A1215347C695E94D1273 |
| 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/lib/python2.7/site-packages/sklearn/linear_model/ridge.pyo |
| FileSize | 36410 |
| MD5 | 295F5EDA3C7B427EE7E3CF72AF56B6D1 |
| SHA-1 | 0006B5777C61B7729CE325B5A815BC406E80EE2F |
| SHA-256 | 5FD72781589F4E6F469874E7620A73E1161FC75CAFB80C3312CC9C509BE9DFA0 |
| SSDEEP | 768:xTap9lTrOaQwIyGC6+6Ugt9F31fV78kQ6M:0FrOaQ1yGCHzgtL31fVQkQp |
| TLSH | T18FF28250BBAA0AABC522817674F402879BB4F07BD64237403AEDE1393FD4679C16F784 |
| 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/metrics/tests/test_score_objects.pyo |
| FileSize | 5117 |
| MD5 | E88BEBFB3F50750CBD289C9E79DB8A7F |
| SHA-1 | 00AAD83D5A8C30BB17581993249AA5C417BFE91C |
| SHA-256 | 9174AC809803FBB9AA1F358701E32FF6E3A63EE1B29B6B78A379DC5CE0B5316A |
| SSDEEP | 96:xMD3TfTG0qxO6xSO+iTzt5scf8bK00CHOq4HbFz4gChaKJQO2QW25ZOiLJRbBnu:xICnO0SO+ift6+TmR47+lJH20u |
| TLSH | T141B10F80A7E68ACBC77A1131B8F043175BA4F877510037857AACF07E27C671AE91638D |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.7/site-packages/sklearn/neural_network/__init__.pyo |
| FileSize | 319 |
| MD5 | 8B04A17EDC82FCFA2FD819451DACF843 |
| SHA-1 | 00C8BC433D6CDB20B5945364316598BB3A9AD62B |
| SHA-256 | 1ED3A80169EA4B940ACBBCA1F1458B7EA8D41166288FCBE8F58DBBEE692A9985 |
| SSDEEP | 6:pPW/xy/OFBvb5XnIOeBA/PyABMhN/fn/43vKMiA9Y3xmDrkA1XXhMRRaBgB:pPW/xCOFBz5Yn2/qDhN/P/43iMiAfcwc |
| TLSH | T102E0CD51967B1F83D4BD2134B140B103859C70B3574326D8250966EF7DD87C9526F4CD |
| 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/feature_selection/__init__.pyo |
| FileSize | 1063 |
| MD5 | 7032ECD7E0B7E4461C973FE54DC04D8F |
| SHA-1 | 01260B7A74EFBCE8C752793166BCFE3E8D02D9F3 |
| SHA-256 | 26D17C821D8FE054482F294C9E178449E437C7C2A93DCC7C05F09948F726DE67 |
| SSDEEP | 24:pPcCdLq0TC70FVyXDJLbM2YBcx8w4olz98y0/E888888U:xnLq07FVyFLbJI4r0c888888U |
| TLSH | T1E511AF821FB657F3C02E5A3398D0053756BC70B619510702A97C31F97FF6682C11D386 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.7/site-packages/sklearn/ensemble/tests/__init__.pyo |
| FileSize | 154 |
| MD5 | CB47BD8F44829662AA57D25470A0F4DA |
| SHA-1 | 01C3E7EA6D31D3CE8A297C2B1857C168F8A9A9F7 |
| SHA-256 | 47709FF08E14B5BB62D66E85563BE63E450B4D448FEFE11430467D7EFB5DAF09 |
| SSDEEP | 3:pPmleh/Tj3tNltNltWiKT9Y3IMmoWrz4Az+iA6BRzaiitn:pPSeh/T4iA9Y3xmDrkAyiAcRaF |
| TLSH | T1A2C02BC0F3334393F63D2939AD00020F81CCC473A045B590740C214F1C4C55D0A3D8C1 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.7/site-packages/sklearn/gaussian_process/correlation_models.pyo |
| FileSize | 8334 |
| MD5 | BEBC0EB527106E47F5E29CE39972AE09 |
| SHA-1 | 0219BE7D317743AB4C12AB0B3428CA21A0070377 |
| SHA-256 | 165359A2AEC85C572D4E91E90F8A7C9390967297D44534E897895A6E160F7B07 |
| SSDEEP | 192:xnCFI7jQp5vuWNj6cvL2T7X2oKvEnOgFZjvwaOjwx1vw3qjrdRsB:xCFIQvvrvL2T2vMthvwa5vw39 |
| TLSH | T1CF02FE829BA9076AE19241B074B26403D966D07B7A929B04379CF4B43FD5F70D93F3C9 |
| Key | Value |
|---|---|
| FileName | ./usr/lib/python2.7/site-packages/sklearn/gaussian_process/tests/__init__.pyo |
| FileSize | 162 |
| MD5 | 10C5C2AF2B5A153C291B4F2DE5185EE7 |
| SHA-1 | 02815929E04DD3AD11581A7E04888033118C0779 |
| SHA-256 | B4F325F18A7F86870DD083E9CB55816D324506675A05765C622F950ADBB5F139 |
| SSDEEP | 3:pPmleh/Tj3tNltNltWe69Y3IMmoWrz4AzwWwO1Rzy6BRzaiitn:pPSeh/T4e69Y3xmDrkAkWFbWcRaF |
| TLSH | T17CC08C80A23B02A3D5795835A600020F55C884735201BAA1650C200A2F4829D0A3E882 |