| Key | Value |
|---|---|
| FileName | ./usr/share/doc/weka-doc/doc/weka/classifiers/rules/part/ClassifierDecList.html |
| FileSize | 18950 |
| MD5 | C3469BCC0C40491541BA58C809DC2A28 |
| SHA-1 | 011637F76BE03537AD951F01CD5D057CA7343BA0 |
| SHA-256 | F7F325AB6345BB7BC1F72E16E4BD3B0DD61CB6B869A01CACC89AE3B63AC9F9A6 |
| SSDEEP | 384:NgPEFicikNJm6mrrciBJsWbXD11aL4m1mnPXCd4DVTPGvZicikNI:NgPE0RhnsWbXD11aLz1mnPXCd4D5PGva |
| TLSH | T1B282833006B73C7206AB42C9AABC2F577AE38865FA106E44B5FDD6362BC1EC5790140B |
| hashlookup:parent-total | 2 |
| hashlookup:trust | 60 |
The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:
| Key | Value |
|---|---|
| FileSize | 4773034 |
| MD5 | A80C6D391FCD9DA3F5C470090E3BFB10 |
| PackageDescription | documentation for the Weka machine learning suite Weka is a collection of machine learning algorithms in Java that can either be used from the command-line, or called from your own Java code. Weka is also ideally suited for developing new machine learning schemes. . Implemented schemes cover decision tree inducers, rule learners, model tree generators, support vector machines, locally weighted regression, instance-based learning, bagging, boosting, and stacking. Also included are clustering methods, and an association rule learner. Apart from actual learning schemes, Weka also contains a large variety of tools that can be used for pre-processing datasets. . This package contains the documentation. |
| PackageMaintainer | Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com> |
| PackageName | weka-doc |
| PackageSection | doc |
| PackageVersion | 3.6.14-1 |
| SHA-1 | 05AD641B6678E7C3013A36E6A0D270C660496875 |
| SHA-256 | D2CD69B7451710481D83C5DBAE746A4ECDDAECF0F3B879ED7763DBE0885875BA |
| Key | Value |
|---|---|
| FileSize | 4773760 |
| MD5 | F762C2285A8EFEFCDBE7B3B2E731050A |
| PackageDescription | documentation for the Weka machine learning suite Weka is a collection of machine learning algorithms in Java that can either be used from the command-line, or called from your own Java code. Weka is also ideally suited for developing new machine learning schemes. . Implemented schemes cover decision tree inducers, rule learners, model tree generators, support vector machines, locally weighted regression, instance-based learning, bagging, boosting, and stacking. Also included are clustering methods, and an association rule learner. Apart from actual learning schemes, Weka also contains a large variety of tools that can be used for pre-processing datasets. . This package contains the documentation. |
| PackageMaintainer | Debian Java Maintainers <pkg-java-maintainers@lists.alioth.debian.org> |
| PackageName | weka-doc |
| PackageSection | doc |
| PackageVersion | 3.6.14-1 |
| SHA-1 | 035BB36EF2ADD95137024A6E4092B2A77D6090FB |
| SHA-256 | 53B621118EF773E031264740267EC072418EF8CB90D9D554DA90BEA6948D895E |