Building the data det for weka.
为WEKA构建数据集。
Creating the regression model with weka.
用WEKA创建一个回归模型。
So now we have the data loaded into WEKA.
现在我们已经将数据载入了WEKA。
WEKA is powerful and 100-percent free to use.
WEKA功能强大且100 %免费。
At this point, please go ahead and install WEKA.
我们继续并安装WEKA。
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。
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。
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。
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。
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。
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。
To load data into WEKA, we have to put it into a format that will be understood.
为了将数据加载到WEKA，我们必须将数据放入一个我们能够理解的格式。
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。
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。
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。
Bigger houses reduce the value - WEKA is telling us that the bigger our house is, the lower the selling price?
较大的房子价格反而低—weka告诉我们房子越大，销售价格越低？
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。
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。
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。
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。
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。
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。
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。
Now that the desired model has been chosen, we have to tell WEKA where the data is that it should use to build the model.
现在，选择了想要的模型后，我们必须告诉WE KA它创建这个模型应该使用的数据在哪里。
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。
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