It is amazing to learn Machine learning and data mining algorithms that are used to design a model. For learning working of different algorithms i started exploring weka tool.
Here is the small example of Weka Explorer.
WEKA is a state-of-the-art facility for developing machine learning (ML) techniques and their application to real-world data mining problems. It is a collection of machine learning algorithms for data mining tasks. The algorithms are applied directly to a dataset. WEKA implements algorithms for datapreprocessing, classification, regression, clustering, association rules; it also includes a visualization tools. The new machine learning schemes can also be developed with this package. WEKA is open source software issued under the GNU General Public License.
This is how Weka GUI looks:
Let’s start with Weka Explorer
Explorer is an environment for exploring data. As shown below at the very top of the window, just below the title bar there is a row of tabs. Only the first tab, ‘Preprocess’, is active at the moment because there is no dataset open. The first three 4 buttons at the top of the preprocess section enable you to load data into WEKA. Data can be imported from a file in various formats: ARFF, CSV, C4.5, binary, it can also be read from a URL or from an SQL database (using JDBC) [4]. The easiest and the most common way of getting the data into WEKA is to store it as Attribute-Relation File Format (ARFF) file.
Now, select any data set. Suppose we select iris data set. After choosing iris data set below window appears.
In ‘Filters’ window, click on the ‘Choose’ button. This will show pull-down menu with a list of available filters. Select Supervised->Attribute -> Discretize and click on ‘Apply’ button. The filter will convert Numeric values into Nominal.
This are the steps of the Weka explorer part. After pre-processing you can move to classify tab for classification. Various learning schemes available in weka include decision trees and lists, instance-based classifiers, support vector machines, multi-layer perceptrons, logistic regression, and bayes’ nets. “Meta”-classifiers include bagging, boosting, stacking, error-correcting output codes, and locally weighted learning.
In the next post we will explore Weka classify Tab.