Weka Assignment Help
Weka is an open-source machine learning software that has been tried and test. It can be accessed through a graphical user interface, a standard terminal application or a JAVA API. Weka is generally used in fields like teaching, research and industrial applications. Weka includes a range of standardized tools for common machine learning tasks and also offers direct access to well-known toolboxes, like scikit-learn, R and Deeplearning4j. Weka comes with integrated assistance, which provides a detailed manual for the users to use and refer to for understanding Weka.
Weka is a set of algorithms for the machine learning of data mining tasks. The algorithms can be either directly applied to a dataset or called from the user’s own Java code. Weka provides tools for pre-processing, classification, regression, clustering, rules of association and visualization that can be applied to real-world data mining problems.
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
To use the latest release of the Weka tool the user needs or requires having JAVA 8 or later installed into their system. Also, if the user’s system is Windows with a display with high pixel density it will be better to use JAVA 9 or later to avoid having any problem to arise in terms of inappropriate scaling of Weka’s graphical user interface (GUI).
Some of the features that Weka tool has are as follows:
- Open Source: Weka is open-source software that has a dual license under GNU GPL; also, Pentaho Corporation has the exclusive license to use the business intelligence platform in its product. Note that the GNU GPL means that in turn, your software would also have to be released as GPL.
- Graphical User Interface: Weka has an option of Graphical User Interface (GUI). This allows the user to complete machine learning projects or tasks without programming. The graphical user interface workbench can be divided into three sub-interfaces, which are explorer, experimenter and knowledge flow.
- Command Line Interface: From the command line, all of the software features and attributes can be used. This can be very beneficial when scripting or working with large jobs or tasks. It is recommended to use the Weka Command Line Interface (CLI) when dealing with in-depth usage and calculation of data. There are certain functionalities available in the command-line interface that is not available in the graphical interface.
- Java API: Since Weka is written in Java, there are APIs provided that are well documented and can be integrated into the user’s own applications.
- Documentation: There books, manuals, wikis and MOOC Assignments that can train you on how to use the platform effectively.
The Weka workbench provides three main ways to work on your problem and data. Those three ways are:
- The Explorer: this interface of the workbench is used for playing around and trying things out on the data.
- The Experimenter: this interface of the workbench is used for controlled experiments on the data to analysis on what the data is trying to indicate or say.
- The Knowledge Flow: this interface of the workbench is used for designing a path or wat for answering the user’s question in a graphical method.
The explorer is where you play around with your data and try to figure out what transforms to apply to your data and what algorithms to run in the experiment stage of the data.
The Explorer interface is split into 6 different sections for working with the data which are as follows:
- Pre-process: this allows the user to choose and load a dataset and manipulate the data into a form that required to work with further on.
- Classify: the classify tab allows the user to select and run different algorithms to operate and experiment on the pre-processed data.
- Cluster: in the cluster tab, it allows or grants the user to select and apply different clustering algorithms on the dataset to identify the possible clusters.
- Associate: the associate tabs help in applying or running association algorithms to extract insights and identify the possible association present in the dataset.
- Select Attributes: this tab allows the user to run attribute selection algorithms on the data set to select those attributes that are relevant to the feature the user want to predict in the experiment section of the workbench.
- Visualize: the visualize tab allows the user to create possible visualization to have a better understanding of the relationship between attributes.
The user cannot switch between the other tabs before completion of the initial pre-processing of the dataset.
The experimenter interface is designed to run experiments and analysis the results based on algorithm and datasets selected by the user. The tool is used for analysing and evaluating outcomes are very efficient, it allows the user to evaluate and compare results over several runs that are statistically significant.
By selecting different algorithms and evaluating the output, the Experimenter interface enables the user to conduct some experiments on the data set. It has the following components:
- Setup: This is the first tab for an interface that is used to set the experiment, i.e. providing the dataset, selecting the algorithm and output destination, etc. If the comparison is required, it is possible to add multiple dataset and compare the output using multiple algorithms or methods.
- Run: the run tab is used to run the experiment that has been set up in the previous tab.
- Analyse: The Analyse tab is used for analysing the result from an experiment that has been executed. The analysed output from this tab can be saved if it is required.
Weka Knowledge Flow
The Knowledge Flow interface provides the users to select WEKA components from a toolbar present in GUI and place them on a layout canvas while providing the option of connecting components into a directed graph that processes and analyzes data based on the set flow. The Knowledge Flow Interface provides an alternative option to the Explorer Interface, in the situation of how data moves through the system for those users who like learning. This interface also allows the user to plan and implement the configurations for broadcast data processing, an option that is not provided by the Explorer Interface. Through selecting Knowledge Flow from the panel options, the user can invoke or call the Knowledge Flow interface.
Knowledge Flow Interface Components
Many of the Knowledge Flow components are similar to the Explorer components. There is a total of 13 components present in the Knowledge Flow Interface. Some of the components are as follows:
- DataSources: This component or folder has all the WEKA’s data source loader options.
- Filters: The Filter folder contains the filters that the user may require
- Classifier: This folder contains all of WEKA’s classifiers that user can possibly require and choose from.
- Clusters: The Clusterers folder holds the clusters methods or functions that the user can select from.
- AttSelection: The AttSelection folder contains evaluators and search methods that are required for attribute selection.
- Associations: The Associations panel holds the association rule learners for the user to select based on the requirement and need
- Evaluation: The Evaluation panel or folder contains the evaluation method or functions to test the output created from the clusters and classifiers.
- Visualization: This folder or panel contains all the visualization methods or function options that the user might select from.
The Knowledge Flow components run in a separate thread of execution. There is a possible exception to the case, where data is being processed incrementally; in this situation, a single thread of execution is used. This is generally due to the amount of processing done per data point is small and launching a separate processing thread will incur large overheads for each.
Weka data formats
The data format that is generally used by Weka is the Attribute Relation File Format for data analysis, which is the default format. However, there are some other formats that Weka supports from where data can be imported. Those formats are listed below:
- Database using ODBC
The Attribute Relation File Format (ARFF) is an ASCII text file representing a list of instances that share a set of attributes. The data format has two parts:
1) Header Section: This section defines the relation name, i.e. the data set, the attribute name and the attribute type.
2) Data Section: The Data section has or contains the data declaration line and the list of the actual data instances lines.