What is Weka

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what is weka

Weka is a collection of data mining machine learning techniques. The algorithms can be applied to a dataset directly or named from Java code. Weka offers data pre-processing, grouping, regression, clustering, association rules, and visualization. It's also a great place to start when developing new machine learning algorithms.

Classification

Classifiers in Weka can predict nominal and numeric quantities. Some of the learning techniques accessible include decision trees and lists, support vector machines, instance-based classifiers, logistic regression, and Bayes' nets. Once the data has been loaded, all tabs become active. Based on the requirements, we may determine which method is best suited to provide an easily understood output.

Before running any classification algorithm, we must first establish test choices. A list of test alternatives is provided below.

Make use of a practice set: The rating is based on how well it can classify the cases it was taught to classify.

The evaluation is based on how well it can use the provided training set to predict the class of a collection of examples loaded from a file.

Cross-validation: Cross-validation is performed using the number of folds entered in the 'Folds' text field.

The evaluation is based on how effectively it can predict a specified percentage of the data, which is held out for testing using the percent field values.

Classifiers are used by Weka to categorize data based on attribute qualities.

Weka Explorer

Figure 4 depicts the Weka Explorer, which is divided into six tabs.

The tabs are listed below.

1) Preprocessing: This allows us to pick the data file we want to use.

2) Classify: This allows us to run different algorithms on preprocessed data files and see how they perform.

3) Cluster: This enables us to use various clustering algorithms to find clusters in the data stream.

4) Association: This enables us to use association rules to find the data's associations.

5) Select attributes: These let us evaluate how the inclusion and deletion of attributes from the experiment affect the results.

6) Visualize: This allows us to see a 2D representation of the data set in the form of a scatter plot and a bar graph.

The user will not be able to switch between tabs until the data set's initial preparation is completed.

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