Creating the regression model with weka.
用WEKA创建一个回归模型。
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。
This tells WEKA that we want to build a regression model.
这会告诉WEKA我们想要构建一个回归模型。
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。
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。
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weka API可以使用的格式。
As you can imagine, the central building block in the WEKA API is going to be the data.
正如您所想，WEKAAPI内的这个中心构建块就是数据。
NOTE: I would warn you ahead of time that the WEKA API can be difficult to navigate at times.
注：我最好提前告诫您WEKAAPI有时很难导航。
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。
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。
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。
This tells WEKA that to build our desired model, we can simply use the data set we supplied in our ARFF file.
这会告诉WEKA为了构建我们想要的模型，可以使用我们在ARFF文件中提供的那些数据。
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。
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。
Load the data file bmw-training.arff into WEKA using the same steps we've used to this point in the Preprocess TAB.
将数据文件bmw - training . arff载入WEKA，步骤与我们之前在Preprocess选项卡中使用的相同。
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。
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。
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。
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。
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。
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。
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。
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。
To take this simple example to the next level, let's take a look at a data file that the WEKA Web site supplies to us as a regression example.
要想把这个简单的示例提升到一个新的级别，让我们来看一下 WEKAWeb站点上作为回归示例提供给我们的一个数据文件。
It also has a general API, so you can embed WEKA, like any other library, in your own applications to such things as automated server-side data-mining tasks.
它还有一个通用API，所以您可以像嵌入其他的库一样将WEKA嵌入到您自己的应用程序以完成诸如服务器端自动数据挖掘这样的任务。
In fact, there's a piece of software that does almost all the same things as these expensive pieces of software — the software is called WEKA (see Resources).
实际上，有一种软件可以实现那些价格不菲的软件所能实现的全部功能 —这个软件就是WEKA（参见参考资料）。
Finally, this article discussed the first data-mining model, the regression model (specifically, the linear regression multi-variable model), and showed how to use it in WEKA.
最后，本文探讨了第一个数据挖掘模型：回归模型(特别是线性回归多变量模型)，另外还展示了如何在WEKA中使用它。
Remember that 100 rows of data with five data clusters would likely take a few hours of computation with a spreadsheet, but WEKA can spit out the answer in less than a second.
如果使用电子数据表处理 100行数据和五个数据群集将会花费几个小时的计算时间，但WEKA在不到一秒钟的时间内就能给出答案。
Remember that 100 rows of data with five data clusters would likely take a few hours of computation with a spreadsheet, but WEKA can spit out the answer in less than a second.
如果使用电子数据表处理 100行数据和五个数据群集将会花费几个小时的计算时间，但WEKA在不到一秒钟的时间内就能给出答案。
应用推荐