Weka Reviews

Name: Weka
Brand: Weka
Rating: 3.8 (14 reviews)

Vendor: Weka

3.8 out of 5

14 reviews
78% willing to recommend

1,557 followers

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What is Weka?

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.

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Weka is the #2 ranked solution in top Data Mining solutions and #4 ranked solution in top Anomaly Detection Tools. PeerSpot users give Weka an average rating of 7.6 out of 10. Weka is most commonly compared to KNIME: Weka vs KNIME. Weka is popular among the large enterprise segment, accounting for 53% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a university, accounting for 18% of all views.

Helped 851,604 peers since 2012

Featured Weka reviews

AwaisAnwar

Treasury Management in Finance Department at National University of Pakistan

I haven't found it particularly useful. It lacks state-of-the-art algorithms and impressive outcomes. While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results. Moreover, a new user interface would be great, especially for beginners. Something that guides them through the available tools and helps them achieve their goals. I haven't seen anything like that myself, though maybe it's there and I missed it.

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Abuto Vincent

Weka Specialist 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.

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Xavier Suriol

Manager, Facilities at XS AMSAFIS DATASETS, S.L.

In a new machine learning job, if the method is a bit foreign to me, if I have to do it in R, it could be a tedious task. First, I need to identify the libraries required for the new methodology. This can involve identifying two, three, or even four libraries. Then, I need to read their manuals thoroughly. This is time-consuming. In Weka, as all machine learning tools are on my desktop, I easily find out the method. As a freelancer, people send me datasets, and I work on the statistics at home before providing the solution. When a solution needs to be implemented on a server, server programmers install it on the server. This is similar to Power BI, where I prepare files on my desktop, and someone else uploads them to the server for others to access. I think I cannot send a Weka solution to a server programmer. In Weka, anyone can run the program without being a programmer, which is a good feature since the entry cost is very low.

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Weka mindshare

As of May 2025, the mindshare of Weka in the Data Mining category stands at 18.9%, down from 20.9% compared to the previous year, according to calculations based on PeerSpot user engagement data.

Data Mining

PeerResearch reports based on Weka reviews

Type	Title	Date
Category	Data Mining	May 20, 2025	Download
Product	Reviews, tips, and advice from real users	May 20, 2025	Download
Comparison	Weka vs KNIME	May 20, 2025	Download
Comparison	Weka vs IBM SPSS Statistics	May 20, 2025	Download
Comparison	Weka vs IBM SPSS Modeler	May 20, 2025	Download

Title	Rating	Mindshare	Recommending
KNIME	4.1	25.2%	94%	60 interviews Add to research
IBM SPSS Statistics	4.0	16.5%	88%	38 interviews Add to research

Valuable Features

Weka provides ease of use with its intuitive interface and extensive algorithm library for data mining, classification, and clustering. It supports integration with new algorithms and facilitates rapid testing and data preprocessing. Users appreciate its ability to handle machine learning tasks without coding. The numerous filters and transformation tools streamline data manipulation. Weka is considered quicker than Python or R, offering accessible machine learning tools and comprehensive documentation suitable for education and quick analysis.

"It is a stable product."
"The interface is very good, and the algorithms are the very best."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."

Room for Improvement

Weka faces challenges in scalability and performance due to Java limitations. Its pre-processing is complicated and lacks deep learning algorithms. Issues arise with handling missing values and visualizations are limited. Users find documentation inconsistent and crave more user-friendly resources. The software struggles with big data integration and lacks support for automation. A more intuitive user interface and enhanced machine learning capabilities are desired. Users indicate stability concerns and difficulty in server deployment.

"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"Weka could be more stable."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."

Pricing

Weka users report that the software is primarily free and open-source, making it an accessible choice for enterprises with varying needs. While the open-source version is widely used, some organizations opt for the paid version to access more advanced features and customization options. Pricing details for the paid version are not widely known among users, as many prioritize the tool's capabilities over cost, utilizing open-source for budget-friendly solutions.

"We use the free version now. My faculty is very small."
"As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if it is a commercial product."
"The solution is free and open-source."

Popular Use Cases

Weka is primarily utilized for machine learning, handling tasks such as classification, clustering, and anomaly detection. Users apply Weka for data exploration, deploying algorithms like regression trees, association rules, and support vector machines. It facilitates quick analysis, even for text data classification, by transforming data types. Popular among academic researchers and businesses, it supports cloud and on-premise deployments. Despite competition from R and Python, it's preferred for rapid approaches by non-programmers.

Service and Support

Weka's customer service relies on a user community and documentation. Users report mixed experiences with support. Some find the technical support responsive and interactive, while others rate it as average, with slow response times. Many rely on forums or platforms like Stack Overflow for assistance. Some users express uncertainty about accessing official support channels. Generally, users can resolve 80% of issues through forums, but specific information may be lacking.

Deployment

Weka's initial setup is straightforward and easy to install, with many finding it intuitive and suitable for those familiar with Java. Installation times vary, ranging from a few minutes to several hours depending on data complexity. Some report challenges with large datasets and documentation, while others note that training and model testing become simpler with experience. Though easy for non-programmers, some find visualizations lacking compared to other tools like Python.

Scalability

Weka is often praised for its machine learning algorithms, yet opinions differ on its scalability. Some users report satisfactory scaling for individual use, while others note challenges with larger datasets and multi-user environments. Horizontal scalability is not natively supported, but vertical expansion is possible. Users highlight the challenge of integrating with external tools for improved scalability. Weka's stability and low maintenance needs are commonly appreciated.

Stability

Users find Weka stable and reliable for their needs. It has performed well over two years according to many, with stability confirmed by colleagues. Stability is contingent on selecting accurate predictor variables for better outcomes. Most users rate its stability highly, with some suggesting better documentation and output for dynamic results. Despite occasional crashes, it's considered stable with resource management. Users suggest improvement areas while acknowledging its stable performance.

These insights are based on the in-depth reviews provided by peers to help you make a better buying decision.

Download our Weka Buyer's Guide for additional reliable information.