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Weka Reviews

Vendor: Weka
3.8 out of 5
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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 #3 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 Business Hub: Weka vs KNIME Business Hub. Weka is popular among the large enterprise segment, accounting for 49% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a educational organization, accounting for 18% of all views.
Buyer's Guide
Weka
August 2025
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Weka mindshare

As of August 2025, the mindshare of Weka in the Data Mining category stands at 17.8%, down from 20.8% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Data Mining Market Share Distribution
ProductMarket Share (%)
Weka17.8%
KNIME Business Hub24.3%
IBM SPSS Modeler17.9%
Other40.0%
Data Mining

PeerResearch reports based on Weka reviews

TypeTitleDate
CategoryData MiningAug 28, 2025Download
ProductReviews, tips, and advice from real usersAug 28, 2025Download
ComparisonWeka vs KNIME Business HubAug 28, 2025Download
ComparisonWeka vs IBM SPSS StatisticsAug 28, 2025Download
ComparisonWeka vs IBM SPSS ModelerAug 28, 2025Download
Suggested products
TitleRatingMindshareRecommending
KNIME Business Hub4.124.3%94%60 interviewsAdd to research
IBM SPSS Statistics4.015.4%88%39 interviewsAdd to research
 
 
Key learnings from peers

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.

Review data by company size

By reviewers
Company SizeCount
Small Business5
Midsize Enterprise1
Large Enterprise2
By reviewers
By visitors reading reviews
Company SizeCount
Small Business78
Midsize Enterprise57
Large Enterprise128
By visitors reading reviews

Top industries

By visitors reading reviews
University
18%
Educational Organization
18%
Computer Software Company
10%
Comms Service Provider
6%
Financial Services Firm
6%
Government
4%
Healthcare Company
4%
Non Tech Company
3%
Recreational Facilities/Services Company
3%
Construction Company
3%
Energy/Utilities Company
2%
Performing Arts
2%
Hospitality Company
2%
Manufacturing Company
2%
Media Company
2%
Transportation Company
2%
Non Profit
2%
Real Estate/Law Firm
2%
Retailer
2%
Logistics Company
1%
Wholesaler/Distributor
1%
Insurance Company
1%
Marketing Services Firm
1%

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Product Categories

Product Categories
Data Mining
Anomaly Detection Tools

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Weka Reviews Summary
Author infoRatingReview Summary
Treasury Management in Finance Department at National University of Pakistan3.5In my university experience, Weka was adequate for basic data analysis but lacked visualizations and advanced features. I preferred Python libraries due to their flexibility and free access, while KNIME would have been better for energy analytics.
Manager, Facilities at XS AMSAFIS DATASETS, S.L.3.5I run methods like Association rules and RandomForest in Weka, mainly using menu options as I don't know Java. While Weka is accessible for non-programmers, I prefer R and Python for most tasks. However, Weka's simplicity is useful for unfamiliar methods.
Weka Specialist at freelancer4.5I use Weka for machine learning tasks because it requires no coding and simplifies setting parameters. While it offers ROI, its visualization needs improvement, particularly regarding data cleaning. Previously, I used Python and R for similar tasks.
Student at a university with 1,001-5,000 employees3.5No summary available
Weka Specialist at freelancer4.5No summary available
Freelance Data Scientist at Freelancer4.0No summary available
Freelance Engineer at Autónomo3.5No summary available
Manager, Facilities at XS AMSAFIS DATASETS, S.L.4.0No summary available