Weka tool tutorial

Halo, kali ini saya akan posting sedikit tutorial penggunaan WEKA. Apa itu WEKA? Jadi WEKA itu semacam tools berisi koleksi dari algoritma Machine Learning beserta tools lainnya untuk preprocessing data dsb. Jadi mengapa memakai WEKA?

Alasan pertama adalah kemudahan. Kita tinggal download, install, dan buka aplikasinya. Tinggal copy file. Ok sekarang mari masuk ke tutorialnya. Pada tutorial ini, kita akan membuat classifier untuk membedakan jenis bunga Iris. Atribut yang digunakan dibagi menjadi 5 4 fitur dan 1 class , yaitu sepal length , sepal width , petal length , dan petal width ; serta atribut terakhir berupa jenis bunga Iris itu sendiri. Keterangan lebih lengkap tentang Iris dataset dapat dilihat pada Wikipedia.

Tampilan awal WEKA bisa dilihat pada gambar berikut. WEKA sudah menyediakan dataset dari Iris yang berupa file.

Setelah itu WEKA akan menampilkan keterangan dan grafik tentang dataset yang di-load. Di tab preprocess ini kita bisa melakukan pemrosesan data seperti normalisasi, standarisasi, downsampling untuk class tertentu, dsb. Weka — is the library of machine learning intended to solve various data mining problems. The system allows implementing various algorithms to data extracts, as well as call algorithms from various applications using Java programming language.

Project goals : creating the modern environment to develop various machine learning methods and implement them in real data, making machine learning methods accessible and available for the wide audience.

The idea is to provide the specialists working in the practical fields with the ability to use machine learning methods in order to extract useful knowledge right from the data, including relatively high volumes of information.

Weka users are researchers in the field of machine learning and applied sciences. It can also be used for various learning purposes. Weka includes a set of tools for the preliminary data processing, classification, regression, clustering, feature extraction, association rule creation, and visualization.

Weka is an efficient tool that allows developing new approaches in the field of machine learning. Weka is an open-source software solution developed by the international scientific community and distributed under the free GNU GPL license. The software is fully developed using the Java programming language. It is expected that the source data are presented in the form of a feature matrix of the objects. Weka offers Explorer user interface, but it also offers the same functionality using the Knowledge Flow component interface and the command prompt.

It also offers a separate Experimenter application that allows comparing predictive features of machine learning algorithms for the given set of tasks. The preprocessing panel allows importing the data from the base, a CSV-file etc.

The associate panel is intended to find all the important interconnection between various characteristics. The cluster panel provides access to the k-means algorithm, EM-algorithm for the Gaussian mixture model etc. The select attributes panel provides access to different characteristics choosing methods. The visualize panel allows creating the scatter plot matrix, making it possible to choose and scale charts etc.