weka
美英
- n.【动】新西兰黑秧鸡
- 网络怀卡托智能分析环境(Waikato Environment for Knowledge Analysis)；数据挖掘工具软件介绍；数据挖掘解决方案
复数：wekas
wekaweka
怀卡托智能分析环境(Waikato Environment for Knowledge Analysis)
...和回归，我介绍了数据挖掘的概念以及免费的开源软件 Waikato Environment for Knowledge Analysis（WEKA），利用它可 …
数据挖掘工具软件介绍
数据挖掘工具软件介绍(weka)数据挖掘工具weka介绍~~数据挖掘工具weka介绍~~隐藏>> 你可能喜欢 文档信息 xia_mingxing贡 …
数据挖掘解决方案
商务智能系统的可行性分... ... 2.5 Dashboard（ 仪表盘）社区版 2.6 Weka（ 数据挖掘解决方案） 2.7 Mordrian OLAP（ 多维 …
不会飞的鸟还有威卡秧鸡
不会飞的鸟还有威卡秧鸡(weka)及濒临灭绝的kakapo鹦鹉（即鸮鹦鹉），这是全世界最大的鹦鹉，它只能爬到低矮的灌木或较 …
数据挖掘软件
...Windows环境下，把分析数据软件(SPSS)、数据挖掘软件(WeKa)与教学评估管理系统(Deiphi)集成到一起，形成了信息管理 …
新西兰秧鸡
沙滩上总是会有这种不怕人的新西兰秧鸡（WEKA）围着你转……TIEKE边看边对照着书本上鸟的种类和知识，我想这将是她人 …
1
Load the data file bmw-training. arff (see Download) into WEKA using the same steps we've used up to this point.
使用我们之前使用过的相同步骤来将数据文件bmw-training.arff（参见下载）载入WEKA。
2
At this point, we are ready to create our model in WEKA.
至此，我们已经准备好可以在WEKA内创建我们的模型了。
3
It's actually quite easy to put our data through the regression model using the WEKA API, far easier than actually loading the data.
实际上使用WEKAAPI让数据通过回归模型得到处理非常简单，远简单于实际加载数据。
4
This article wraps up the three-article series introducing you to the concepts of data mining and especially to the WEKA software.
本文是由三篇文章组成的系列文章的终结篇，该系列向您介绍了数据挖掘的概念尤其是WEKA软件。
5
As you've seen, WEKA can do many of the data mining tasks that were previously available only in commercial software packages.
正如您所见，WEKA可以完成很多在商业软件包中才能完成的数据挖掘任务。
6
In the previous two articles in this " Data mining with WEKA" series, I introduced the concept of data mining.
在这个“用WEKA进行数据挖掘”系列之前的两篇文章中，我介绍了数据挖掘的概念。
7
This article also introduced you to the free and open source software program WEKA.
本文还向您介绍了一种免费的开源软件程序WEKA。
8
Within the warm stomach of the rainforests, kiwi, weka, and the other birds foraged for huhu and similar succulent insects.
在雨林温暖的腹部，鹬鸵、短翼秧机和其他鸟禽找寻甲虫幼虫和类似的多汁昆虫。
9
When we click Start this time, WEKA will run this test data set through the model we already created and let us know how the model did.
当我们这次单击Start时，WEKA将会贯穿我们已经创建的这个模型运行测试数据集并会让我们知道模型的情况。
10
Part 3 will bring the " Data mining with WEKA" series to a close by finishing up our discussion of models with the nearest-neighbor model.
第3部分是“用WEKA进行数据挖掘”系列的结束篇，会以最近邻模型结束我们对模型的讨论。
11
Load the data file bmw-browsers. arff into WEKA using the same steps we used to load data into the Preprocess tab.
采用与将数据加载到Preprocess选项卡时的相同步骤来将数据文件bmw-browsers.arff加载到WEKA内。
12
In this view, WEKA allows you to review the data you're working with.
在这个视图中，WEKA允许您查阅正在处理的数据。
13
When you start WEKA, the GUI chooser pops up and lets you choose four ways to work with WEKA and your data.
在启动WEKA时，会弹出GUI选择器，让您选择使用WEKA和数据的四种方式。
14
Listing 4 shows how the data is formatted to be consumed by WEKA.
清单4显示了如何格式化数据以便为WEKA所用。
15
Hopefully, after reading this series, you will be inspired to download WEKA and try to find patterns and rules from your own data.
希望，在阅读完本系列后，您能跃跃欲试地下载WEKA并尝试从您自己的数据中找到模式和规则。
16
The math behind the method is somewhat complex and involved, which is why we take full advantage of the WEKA.
此方法背后的算法多少有些复杂和难懂，这也是我们为何要充分利用WEKA的原因。
17
The WEKA stand-alone application itself just calls the underlying WEKA Java API, so you've seen the API in action already.
WEKA独立应用程序本身只调用底层的WEKAJavaAPI，所以您应该已经看到过这个API的运转了。
18
So it's important to change WEKA machine learning platform into a data mining research and application platform.
完善WEKA平台对数据挖掘的研究与应用具有重要意义。
19
You can't beat a deal like that, since you can quickly get WEKA up and running and crunching your data in no time.
像这样的好事绝无仅有，因为您可以迅速启动WEKA并即刻就开始处理您的数据。
20
So let's see how to get our data into a format that the WEKA API can use.
那么让我们看看如何将我们的数据转换成WEKAAPI可以使用的格式。
21
But this scratch gets you acquainted with the concept and suffice for our WEKA tests in this article.
但我们的简介让您充分熟悉了这个概念，已足够应付本文中WEKA试用。
22
Now that you're familiar with how to install and start up WEKA, let's get into our first data-mining technique: regression.
在熟悉了如何安装和启动WEKA后，让我们来看看我们的第一个数据挖掘技术：回归。
23
Yet, the results we get from WEKA indicate that we were wrong.
然而，我们从WEKA获得的结果表明我们错了。
24
Finally, the last point I want to raise about classification before using WEKA is that of false positive and false negative.
在使用WEKA前，有关分类我还想指出最后一点，那就是假正和假负。
25
Ideally, this little section should greatly interest you into looking how to integrate WEKA into your own server-side code.
我们希望这一小节能够让您产生将WEKA集成到您自己的服务器端代码的兴趣。
26
After selecting the file, your WEKA Explorer should look similar to the screenshot in Figure 3.
在选择了文件后，WEKAExplorer应该类似于图3中所示的这个屏幕快照。
27
In our previous articles, we use WEKA as a stand-alone application.
在我们之前的文章中，我们将WEKA用作一种独立的应用程序。
28
Let's get some real data and take it through its paces with WEKA.
让我们现在开始获得一些真正的数据并将其带入WEKA。
29
Let's put our data through the regression model and make sure the output matches the output we computed using the Weka Explorer.
让我们把我们的数据通过回归模型进行处理并确保输出与我们使用WekaExplorer计算得到的输出相匹配。
30
Listing 4 shows the ARFF data we'll be using with WEKA.
清单4显示了我们在WEKA中所使用的ARFF数据。