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Weka pros and cons

Vendor: Weka

3.8 out of 5

14 reviews
78% willing to recommend

Pros & Cons summary

Weka offers impressive clustering capabilities with algorithms like DBSCAN, AVM, and RFT, excelling in ease of use for machine learning tasks. Its lightweight build requires minimal resources and delivers quick results, catering to users without coding skills at a low cost. Despite these advantages, Weka lacks newer deep learning algorithms and struggles with big data scalability, data import issues, and limited data transformation tools, making it less ideal for complex, large-scale projects.

Buyer's Guide

Get pricing advice, tips, use cases and valuable features from real users of this product.

Prominent pros & cons

PROS

Weka offers a wide range of algorithms for clustering, making it adaptable to various user requirements.

It is praised for its lightweight nature, enabling efficient algorithm implementation without requiring extensive memory and space.

Weka simplifies the use of complex algorithms on large datasets, providing ease in performing machine learning tasks.

With Weka, data preprocessing options like normalization and standardization are simplified, enhancing data handling efficiency.

Weka allows easy parameter setting and machine learning task completion without programming expertise, making it accessible to all users.

CONS

Some newer algorithms, particularly those related to deep learning, are not available in Weka libraries. Users may need to switch to Python for deep learning tasks.

Weka struggles with datasets that contain a single missing value in a numeric attribute, mistakenly converting numeric fields to strings.

The filter section lacks tools to transform variables, such as converting numeric variables to categorical ones.

Scalability is limited, making it challenging for Weka to handle a large number of users effectively.

There is an unclear integration with Spark, hindering analysis of big data in cluster environments.

Weka Pros review quotes

SK

Samir Kumar Singha

Solution Architect / Data Scientist (upwork) at Freelancer

Nov 10, 2020

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.

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KR

Kh.Ehsanur Rahman

Freelance Data Scientist at Freelancer

May 15, 2022

Weka is a very nice tool, it needs very small requirements. If I want to implement something in Python, I need a lot of memory and space but Weka is very lightweight. Anyone can implement any kind of algorithm, and we can show the results immediately to the client using the one-page feature. The client always wants to know the story. They want the result.

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AV

Weka Specialist at freelancer

Nov 12, 2020

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.

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XS

Manager at XS AMSAFIS DATASETS, S.L.

Nov 18, 2020

I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka.

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DW

Dilhani Withanage

Data Scientist - Upwork at Freelancer

Nov 15, 2020

Working with complicated algorithms in huge datasets is really easy in Weka.

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AS

Data Science at Freelancer on UpWork

Nov 16, 2020

The path of machine learning in classification and clustering is useful. The GUI can get you results. No programming is needed. No need to write down your script first or send to your model or input your data.

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CR

ChristianRivera

Freelance Engineer at Autónomo

Nov 22, 2020

There are many options where you can fill all of the data pre-processing options that you can implement when you're importing the data. You can also normalize the data and standardize it in an easier way.

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reviewer1910160

Student at a university with 1,001-5,000 employees

Jul 19, 2022

It doesn’t cost anything to use the product.

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Oleksandr Ochkasov

Consultant for the implementation of maintenance management and repair of equipment at IT-Enterprise

Aug 15, 2022

Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data.

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AV

Weka Specialist at freelancer

Jan 18, 2023

Weka eliminates the need for coding, allowing you to easily set parameters and complete the majority of the machine learning task with just a few clicks.

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Weka Cons review quotes

SK

Samir Kumar Singha

Solution Architect / Data Scientist (upwork) at Freelancer

Nov 10, 2020

I believe is there are a few newer algorithms that are not present in the Weka libraries. Whereas, for example, if I want to have a solution that involves deep learning, so I don't think that Weka has that capability. So in that case I have to use Python for ... predict any algorithms based on deep learning.

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KR

Kh.Ehsanur Rahman

Freelance Data Scientist at Freelancer

May 15, 2022

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.

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AV

Weka Specialist at freelancer

Nov 12, 2020

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.

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Free Report: Weka Reviews and More

Learn what your peers think about Weka. Get advice and tips from experienced pros sharing their opinions. Updated: January 2026.

881,082 professionals have used our research since 2012.

XS

Manager at XS AMSAFIS DATASETS, S.L.

Nov 18, 2020

Not particularly user friendly.

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DW

Dilhani Withanage

Data Scientist - Upwork at Freelancer

Nov 15, 2020

Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well.

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AS

Data Science at Freelancer on UpWork

Nov 16, 2020

If there are a lot more lines of code, then we should use another language.

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CR

ChristianRivera

Freelance Engineer at Autónomo

Nov 22, 2020

The product is good, but I would like it to work with big data. I know it has a Spark integration they could use to do analysis in clusters, but it's not so clear how to use it.

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reviewer1910160

Student at a university with 1,001-5,000 employees

Jul 19, 2022

A few people said it became slow after a while.

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Oleksandr Ochkasov

Consultant for the implementation of maintenance management and repair of equipment at IT-Enterprise

Aug 15, 2022

Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science.

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AV

Weka Specialist at freelancer

Jan 18, 2023

The visualization of Weka is subpar and could improve. Machine learning and visualization do not work well together. For example, we want to know how we can we delete empty cells or how can we fill in the empty cells without cleaning the data system and putting it together.

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Show 3 more reviews (out of 13)

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