2020-11-10T08:17:00Z

What do you like most about Weka?

Julia Miller - PeerSpot reviewer
  • 0
  • 4
PeerSpot user
Get the report
Helped 765,234 peers since 2012
13

13 Answers

AwaisAnwar - PeerSpot reviewer
Real User
Top 10Leaderboard
2023-12-27T11:28:08Z
Dec 27, 2023

It is a stable product.

Search for a product comparison
DR
Real User
Top 20
2023-08-15T07:44:00Z
Aug 15, 2023

The interface is very good, and the algorithms are the very best.

XS
Real User
Top 20Leaderboard
2023-02-28T12:54:00Z
Feb 28, 2023

In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low.

AV
Real User
Top 20Leaderboard
2023-01-18T15:48:54Z
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.

Oleksandr Ochkasov - PeerSpot reviewer
Consultant
Top 20Leaderboard
2022-08-15T15:45:54Z
Aug 15, 2022

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

NE
Real User
Top 20Leaderboard
2022-07-19T01:54:26Z
Jul 19, 2022

It doesn’t cost anything to use the product.

Learn what your peers think about Weka. Get advice and tips from experienced pros sharing their opinions. Updated: March 2024.
765,234 professionals have used our research since 2012.
CR
Real User
2020-11-22T17:25:00Z
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.

AS
Real User
2020-11-16T16:22:00Z
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.

DW
Real User
2020-11-15T13:54:20Z
Nov 15, 2020

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

XS
Real User
Top 20Leaderboard
2020-11-13T13:00:00Z
Nov 13, 2020

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

AV
Real User
Top 20Leaderboard
2020-11-12T22:34:47Z
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.

KR
Real User
2020-11-12T15:15:00Z
Nov 12, 2020

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.

SK
Real User
2020-11-10T08:17:00Z
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.

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.
Download Weka ReportRead more