Introduction to the Explorer in WEKA

1. The Explorer

2. Explorer: The Interface

4. Comma Separated Value (CSV) format can be converted to ARFF with the addition of @relation, @attribute and @data tags to it

7. Building a Decision Tree Select the Classify tab Click Choose to select the classifier Select the appropriate Test options Click start The result will be shown on the right after a small delay With the weather data and using J48 classifier with Cross – Validation the result is:

9. Working with models We can use the Result list to: Load/Save models Save result buffer Visualization of margin curve, cost curve etc. Visualizing trees and classification errors

10. Exploring the Explorer The functions of the 6 tabs: Preprocess: Choose the dataset and modify it in various ways Classify: Train learning schemes that perform classification or regression and evaluate them Cluster Associate: Get association rules and evaluate them Select attributes: Select the most relevant aspects in the data set Visualize: View 2-d visualization of the data

11. Filtering Algorithms One can select the filter in Preprocess tab and press Choose button to select a filter We have two kinds of filter: Supervised Unsupervised In each filter we have further two options of instances to work with instances and attributes to work with attributes

12. Filtering Algorithms Unsupervised attribute filters We will demonstrate adding attributes Select the Add attribute option from Choose -> Unsupervised->attributes Apply it Initially its value is considered missing in all instances Unsupervised instance filtering We will demonstrate randomizingand re-sampling instances

13. Filtering Algorithms

14. Classify We can access the various Classification algorithms from the Classify tab We can change parameters to meet our needs We will demonstrate Bayesian networks, Naïve Bayesian

15. Classify

16. Clustering We can access the various clustering algorithms from the Cluster tab We can change parameters to meet our needs We will demonstrate kmeans clustering with k = 2

17. Association Rules Association can be accessed from the Associate tab We have three association rule learners We will demonstrate Apriori rule learner

18. Visualization Using this we can visualize the data The attributes are compared against each other. Ease of changing the attributes against the axis and adjust the noise as well.

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