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项目描述

Milk is a machine learning toolkit in Python. Its focus is on supervised classification with several classifiers available: SVMs (based on libsvm), k-NN, random forests, and decision trees. It also performs feature selection. These classifiers can be combined in many ways to form different classification systems. For unsupervised learning, milk supports k-means clustering and affinity propagation.

系统要求

System requirement is not defined
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2011-02-11 01:21
0.3.7

Logistic回归增加了。演示中包含源代码和文档。集群协议标准增加了。当使用错误的nfoldcrossvalidation的起源参数是固定的。
标签: Minor, bugfix
Logistic regression was added. Demos are included in the source and documentation. Cluster agreement metrics were added. An nfoldcrossvalidation bug when using the origins parameter was fixed.

2010-12-18 07:17
0.3.6

新功能:无监督(1级)核密度模型,一重选择一些学习者,学习者树桩,与Adaboost。一种修正当SDA返回空。
标签: Minor, bugfix
New features: unsupervised (1-class) kernel density modeling, a weights option to some learners, stump learner, and Adaboost. A fix for when SDA returns empty.

2010-11-04 16:07
0.3.5

一个修复被列入64位的机器。在measures.py功能有新的接口。
标签: Minor, Minor bugfixes
A fix was included for 64-bit machines. Functions in measures.py have a new interface.

2010-11-01 18:34
0.3.4

随机森林学习者增加了。决策树是加快了20倍。 Gridsearch快得多,因为它没有找到一个最佳的计算所有的折叠。
标签: Stable, Minor
Random forest learners were added. Decision trees were sped up by 20 times. Gridsearch is much faster since it finds an optimum without computing all folds.

2010-10-23 14:46
0.3.3

丢失的文件,阻止安装被列入。
标签: Stable, Minor
A missing file that prevented installation was included.

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