weka造句
1 So now we have the data loaded into WEKA.
2 In this view, WEKA allows you to review the data you're working with.
3 To load data into WEKA, we have to put it into a format that will be understood.
4 This tells WEKA that to build our desired model, we can simply use the data set we supplied in our ARFF file.
5 This tells WEKA that we want to build a regression model.
6 Yet, the results we get from WEKA indicate that we were wrong.
7 So let's see how to get our data into a format that the WEKA API can use.
8 Ideally, this little section should greatly interest you into looking how to integrate WEKA into your own server-side code.
9 Hopefully, after reading this series, you will be inspired to download WEKA and try to find patterns and rules from your own data.
10 The math behind the method is somewhat complex and involved, which is why we take full advantage of the WEKA.
11 It's actually quite easy to put our data through the regression model using the WEKA API, far easier than actually loading the data.
12 You can't beat a deal like that, since you can quickly get WEKA up and running and crunching your data in no time.
13 This article also introduced you to the free and open source software program WEKA.
14 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.
15 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.
16 In fact, I highly recommend if you get involved in using WEKA on your server, you spend some time doing that, since working with data in this way is tedious.
17 In the previous two articles in this " Data mining with WEKA" series, I introduced the concept of data mining.
18 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.
19 This article wraps up the three-article series introducing you to the concepts of data mining and especially to the WEKA software.
20 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.