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Weka pros and cons

Vendor: Weka
3.8 out of 5
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Weka Pros review quotes

AV
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.
SK
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.
DW
Nov 15, 2020
Working with complicated algorithms in huge datasets is really easy in Weka.
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KR
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.
CR
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.
XS
Mar 8, 2023
In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low.
AwaisAnwar - PeerSpot reviewer
Dec 27, 2023
It is a stable product.
reviewer1910160 - PeerSpot reviewer
Jul 19, 2022
It doesn’t cost anything to use the product.
AS
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.
AV
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.
 

Weka Cons review quotes

AV
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.
SK
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.
DW
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.
Learn what your peers think about Weka. Get advice and tips from experienced pros sharing their opinions. Updated: October 2024.
816,406 professionals have used our research since 2012.
KR
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.
CR
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.
XS
Mar 8, 2023
In terms of scalability, I think Weka is not prepared to handle a large number of users.
AwaisAnwar - PeerSpot reviewer
Dec 27, 2023
While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results.
reviewer1910160 - PeerSpot reviewer
Jul 19, 2022
A few people said it became slow after a while.
AS
Nov 16, 2020
If there are a lot more lines of code, then we should use another language.
AV
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.