In this chapter, let us look into various functionalities that the explorer provides for working with big data.
When you click on the Explorer button in the Applications selector, it opens the following screen −
On the top, you will see several tabs as listed here −
Under these tabs, there are several pre-implemented machine learning algorithms. Let us look into each of them in detail now.
Initially as you open the explorer, only the Preprocess tab is enabled. The first step in machine learning is to preprocess the data. Thus, in the Preprocess option, you will select the data file, process it and make it fit for applying the various machine learning algorithms.
The Classify tab provides you several machine learning algorithms for the classification of your data. To list a few, you may apply algorithms such as Linear Regression, Logistic Regression, Support Vector Machines, Decision Trees, RandomTree, RandomForest, NaiveBayes, and so on. The list is very exhaustive and provides both supervised and unsupervised machine learning algorithms.
Under the Cluster tab, there are several clustering algorithms provided - such as SimpleKMeans, FilteredClusterer, HierarchicalClusterer, and so on.
Under the Associate tab, you would find Apriori, FilteredAssociator and FPGrowth.
Select Attributes allows you feature selections based on several algorithms such as ClassifierSubsetEval, PrinicipalComponents, etc.
Lastly, the Visualize option allows you to visualize your processed data for analysis.
As you noticed, WEKA provides several ready-to-use algorithms for testing and building your machine learning applications. To use WEKA effectively, you must have a sound knowledge of these algorithms, how they work, which one to choose under what circumstances, what to look for in their processed output, and so on. In short, you must have a solid foundation in machine learning to use WEKA effectively in building your apps.
In the upcoming chapters, you will study each tab in the explorer in depth.