1.【动】新西兰黑秧鸡
1.a fast-running flightless bird of the rail family with mainly brown and black feathers.
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。