Weka Room for Improvement

Abuto Vincent
Data Scientist at Freelancer
If you were to open the software, there's a section written filter. Then you'd choose your filtering. The filter section lacks some specific transformation tools. If you want to change a variable from a numeric variable to a categorical variable, you don't have a feature that can enable you to change a variable from a numeric variable to a categorical variable. This needs to be improved. Also, when you go to classification, there are some cases in which, under any employed data, under the classification section that you can not actually use tests data alone or trend data alone. Under classification and clustering as well, they should give options to only supply when you're making classification or performing classification on a dataset, then there needs to be an option to either use at trend data first, and then you supply a test data later on. If they went full open-source, like Python and R, it would help the growth of the solution. View full review »
Samir Kumar Singha
Solution Architect / Data Scientist (upwork) at Freelancer
I believe there are a few newer algorithms that are not present in the Weka libraries. If I want to have a solution that involves deep learning, I don't think that Weka has that capability. In that case, I have to use Python to predict any algorithms based on deep learning. View full review »
Dilhani Withanage
Data Scientist - Upwork at Freelancer
More accurate documentation should be published by the Weka company — that would be really helpful. When it comes to data visualization, I think there are lots of ways in which the data could be visualized, like pie charts. There are many more, but within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well. If they could improve that area, I think it would be really good. They should focus more on data visualization, that would be really great as I have experienced many issues relating to this. View full review »
Find out what your peers are saying about Weka, Knime, IBM and others in Data Mining. Updated: November 2020.
447,654 professionals have used our research since 2012.
Kh.Ehsanur Rahman
Freelance Data Scientist at Freelancer
If you have one missing value in your dataset and this missing value belongs to a specific attribute and the attribute is a numeric attribute and there is only one missing data, whenever you import this data, the problem is that Weka cannot understand that this is a numeric field. It converts everything into a string, and there is no way to convert the string into numerical math. It's really very complicated. You will be lucky enough if you get clean data. Every time we get this kind of data with missing values, if we try to understand how many missing datasets there are if it is very less, we just remove this from the dataset itself before importing that. There is no use of algorithm pipelines. In Python, we create a pipeline. First, we use that kind of clustering algorithm, suppose K means clustering, based on that specific cluster, we can choose one cluster. And based on that cluster, we can implement an algorithm. This pipeline is missing in Weka. There is also a problem with the visualization. It only can do only two or three types of visualizations. View full review »
AbdulSamad
Data Science at Freelancer on UpWork
I think there is a little bit of space for improvement. View full review »
Xavier Suriol
Freelancer at Freelancer
Help documentation could be more user friendly. For instance, all ordinary manuals in R follow the same structure, with examples ready to be run and many times with the interpretation of the outputs. For some packages, R has the so-called “Vignettes”, with plenty of explanations and pictures, like in a book. I don’t think Weka has such examples. In Weka packages, documentation is not so “uniform”, not the same structure, as written by different (free style) authors. View full review »
Find out what your peers are saying about Weka, Knime, IBM and others in Data Mining. Updated: November 2020.
447,654 professionals have used our research since 2012.