Weka Overview

Weka is the #3 ranked solution in our list of top Anomaly Detection Tools. It is most often compared to KNIME: Weka vs KNIME

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.
Weka Buyer's Guide

Download the Weka Buyer's Guide including reviews and more. Updated: May 2021

Pricing Advice

What users are saying about Weka pricing:
  • "Currently, I am using an open-source version so I don't know much about the price of this solution."

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Data Scientist at Freelancer
Real User
Top 5Leaderboard
Relatively stable with excellent accuracy and there's no need to know coding

What is our primary use case?

I've handled different projects with this solution. After college, I've handled different projects. The most recent project that I handled was for a company from India. They were looking for a measure classification in regards to the type of engines that cars have, and the pollution levels that they have. There was a mixture of text data that had to be classified. There was the need to transform the text data to a data type that would be easily classified. When employing text data you can't do classification directly. I had to clean the data and program all the variables to suit the required… more »

Pros and Cons

  • "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."
  • "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."

What other advice do I have?

The solution is a desktop application. I did not deploy it on the cloud, actually. It's an application that is on my desktop, on my laptop. If they want their task done faster, and they do not have enough coding expertise, this is definitely an excellent solution to choose from. If they want additional experience because Python and R might be a good option. With Weka, it looks like you're using maybe something like a Microsoft power BI. With Python or R you're actually giving a data scientist a run for his money as things change every day and things evolve and you have to dig deeper, you have…
SK
Solution Architect / Data Scientist (upwork) at Freelancer
Real User
Top 20
Has a good machine algorithm for clustering systems but is lacking a few newer algorithms

What is our primary use case?

Weka is a machine learning tool where we can use supervised and unsupervised learning tools to detect anomalies, for clustering, or classification algorithm. The deployment method depends on the business's requirements. When I worked at the Air Force, it was all cloud. I deployed it on the cloud but that was treated as on-premise because that is confined within the Air Force. It depends upon the requirement of the user. If they want it on-premise, I can provide that. If they want it to be hosted on AWS or any other cloud services, that can also be done.

Pros and Cons

  • "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."
  • "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."

What other advice do I have?

Weka is pretty comprehensive and easy to use. This is the first time that I used machine learning. I have a master's in technology. I analyze small data to get insights into algorithms. I learned a lot from all the files, then I implemented those into a Dell program. It has many features that are not available and there is not much development since it is open source. It should be developed faster. I would rate Weka a six out of ten for these reasons.
Learn what your peers think about Weka. Get advice and tips from experienced pros sharing their opinions. Updated: May 2021.
509,641 professionals have used our research since 2012.
DW
Data Scientist - Upwork at Freelancer
Real User
Top 10
Straightforward and easy-to-use, but not as easy-to-use as other solutions

What is our primary use case?

I work a lot with university students. One of the latest projects I did was related to a classification problem. I had to use different algorithms such as neural networks, Support Vector Machines, nearest neighbor algorithm, decision trees — those types of different algorithms in order to do the machine learning parts. I can't remember the exact data set that I recently worked with, but when it comes to machine learning and data mining, I have worked with different data sets. I use many algorithms in Weka in order to train and test those data sets.

Pros and Cons

  • "Working with complicated algorithms in huge datasets is really easy in Weka."
  • "Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."

What other advice do I have?

The basic configuration is very easy. Compared to writing code in Jupyter Notebook, it's really easy to handle and work with very complicated algorithms in Weka. There are some steps that are not very simple, but overall, it's very easy. It's easy to load data and implement different algorithms with Weka. From my experiences so far, that's the basic advantage with Weka — it's easy to use, easy to handle, and once you learn it, it's not that hard to work with. Working with complicated algorithms in huge datasets is really easy in Weka. Training datasets is equally easy and it's quite speedy as…
KR
Freelance Data Scientist at Freelancer
Real User
Top 20
Can plug in any machine learning algorithm and it works perfectly but needs better visualization

What is our primary use case?

My domain is pure data analysis and data science machine learning. The first time I used Weka, five years back, I did a research project. I prefer to work with Weka whenever I have small and clear projects. Weka is a very nice tool and it helped me to solve any machine learning problem in one minute. In case of machine learning algorithms, classification, or support machines, I used to use this tool to implement those algorithms. Whenever I get any work on any other platform suppose in hours. So what I initially do, I ran the data set in the Weka platform first. It gives me a clear view that… more »

Pros and Cons

  • "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."
  • "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."

What other advice do I have?

Weka is a very simple tool and it has built-in algorithms, which we need do not to implement. It gives concise results that we can display to our clients. Weka is also a very useful tool for filtering. There are a set of built-in filters that we can use to filter our data. If you want to take a sample set of data, suppose a specific percentage of data, or if we want to convert a specific data type to another data type, Weka has good filtering features. We can also use cluster and association rules in Weka. These are the advantages of Weka. If I compared this with R and Python, both can do…
Freelance Engineer at Autonomo
Real User
Top 5Leaderboard
You can standardize data in an easier way but it should work with big data

What is our primary use case?

I used Weka for my Master's thesis. I've used it a couple of times for my personal usage or a quick analysis or graph. You can do a reselection quicker and you can get the graph and put it in our report and do classification. If any project is present, I could develop it.

Pros and Cons

  • "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."
  • "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."

What other advice do I have?

Weka is good to start in data mining. The base had to be clear with base concepts about the models or algorithms you are going to use. You want to test or do some research first. But for production, it's not the best option. It would be a good tool for prototyping. Knime is the best tool for data mining. Weka is good for structured table data. You can use many supervised or unsupervised algorithms, but it's very difficult to get interpretable results about the multilayer option it has. It's not so easy to understand the neural networks if you work with Weka. It would be better to work with…
AS
Data Science at Freelancer on UpWork
Real User
Top 10
An excellent tool for data classification and clustering

What is our primary use case?

I have only used Weka for classification and clustering. I have also used classification with embossing.

Pros and Cons

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

What other advice do I have?

I would give Weka a nine out of ten.
XS
Freelancer at Freelancer
Real User
Top 5Leaderboard
Very easy to implement with great regression trees and association rules.

What is our primary use case?

I mainly use this solution for regression trees, and for association rules. Also, some descriptive statistics because they are very easy.

Pros and Cons

  • "I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
  • "Not particularly user friendly."

What other advice do I have?

My main recommendation is that if you want artificial intelligence, or machine learning, go for an easy and quick tool like Weka, otherwise, any language will have a more expensive entry cost. I would rate this solution an eight out of 10.
Buyer's Guide
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