Weka Valuable Features

Abuto Vincent
Data Scientist at Freelancer
The features that I found most valuable are the classification features. They have a lot of information and a lot of intel. With classification, there's always a chance to split the data into two datasets. You can split one metadata into 92 datasets during that train or test, and the performance can easily be identified after you've trained a model. With clustering, if it's a yes, it's a yes, if it's a no, it's a no. It gives you a 100% level of accuracy of a model that has been trained, and that is in most cases, usually misleading. Classification is highly valuable when done as opposed to clustering. View full review »
Samir Kumar Singha
Solution Architect / Data Scientist (upwork) at Freelancer
I like the machine algorithm for clustering systems. Weka has larger capabilities. There are multiple algorithms that can be used for clustering. It depends upon the user requirements. For clustering, I've used DBSCAN, whereas, for supervised learning, I've used AVM and RFT. Weka is useful for analyzing any data set you want to analyze or if you want to run algorithms of small data sets. When it comes to the enterprise solution, you can use Weka libraries or at least this algorithm that is very available in the Weka libraries. In Java, I can manipulate all these algorithms and the libraries of Weka to produce the desired result for a customer. View full review »
Kh.Ehsanur Rahman
Freelance Data Scientist at Freelancer
Performance is one of the most valuable features. I can plug in any machine learning algorithm and it works perfectly. It is very easy to use the filter. Weka has a good number of filter options. If I compare it with other platforms, I can just use a filter with Weka and apply it. If I want to just convert the numeric data into categorical data, is only a one minute job. We just use a filter and it works. In other platforms, I need to write at least four or five lines of code and I have to check the data. I'm very comfortable with Weka, for these kinds of things, especially their filter and their Classification Algorithm. They have a good number of algorithms. If I want to do this kind of thing with Python, it will take 20 lines of code minimum. View full review »
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AbdulSamad
Data Science at Freelancer on UpWork
The path of machine learning in classification and clustering is useful. The GUI can get you results. No programming is needed. There is no need to write down your script first or send to your model or input your data. Weka classification is very valuable, it gives results, like prediction results. Weka is a little bit better than other tools I have expertise on. Weka is just much better for the classification path and clustering path. If you are going with some predictions that a procedure recalls, it's better than any other tool like R Programming and Python. In machine learning, like deep learning, if the network works, I can run it with the console buttons. View full review »
Xavier Suriol
Freelancer at Freelancer
It is quicker than using languages like R and Python. Wizards make the job more "comfortable" than syntax in languages. View full review »
Find out what your peers are saying about Weka, Knime, IBM and others in Data Mining. Updated: November 2020.
446,956 professionals have used our research since 2012.