Try our new research platform with insights from 80,000+ expert users
Weka Logo

Weka pros and cons

Vendor: Weka
3.9 out of 5
Badge Ranked 1

Pros & Cons summary

Buyer's Guide

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

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

it_user166137 - PeerSpot reviewer
CEO with 11-50 employees
Jul 25, 2015
Weka is a very easy to use Data Mining solution, great for learning and for doing small experiments before exploring the data deeper, with a large number and diversity of algorithms that make it an excellent solution for rapid testing.
SK
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.
KR
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.
Learn what your peers think about Weka. Get advice and tips from experienced pros sharing their opinions. Updated: February 2026.
884,873 professionals have used our research since 2012.
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.
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.
DW
Data Scientist - Upwork at Freelancer
Nov 15, 2020
Working with complicated algorithms in huge datasets is really easy in Weka.
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.
CR
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.
reviewer1910160 - PeerSpot reviewer
Student at a university with 1,001-5,000 employees
Jul 19, 2022
It doesn’t cost anything to use the product.
Oleksandr Ochkasov - PeerSpot reviewer
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.
 

Weka Cons review quotes

it_user166137 - PeerSpot reviewer
CEO with 11-50 employees
Jul 25, 2015
Scalability and performance are the main aspect of improvement in Weka, since it has the main Java limitations, regarding the JVM.
SK
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.
KR
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.
Learn what your peers think about Weka. Get advice and tips from experienced pros sharing their opinions. Updated: February 2026.
884,873 professionals have used our research since 2012.
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.
XS
Manager at XS AMSAFIS DATASETS, S.L.
Nov 18, 2020
Not particularly user friendly.
DW
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.
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.
CR
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.
reviewer1910160 - PeerSpot reviewer
Student at a university with 1,001-5,000 employees
Jul 19, 2022
A few people said it became slow after a while.
Oleksandr Ochkasov - PeerSpot reviewer
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.