We performed a comparison between IBM SPSS Modeler and Weka based on real PeerSpot user reviews.
Find out in this report how the two Data Mining solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."We have full control of the data handling process."
"The ease of use in the user interface is the best part of it. The ability to customize some of my streams with R and Python has been very useful to me, I've automated a few things with that."
"Automation is great and this product is very organized."
"It's a very organized product. It's easy to use."
"It’s definitely scalable, it’s all on the same platform, it’s well integrated. I think the integration is important in terms of scalablility because essentially, having the entire suite helps a lot to scale it"
"Automated modelling, classification, or clustering are very useful."
"I think the code modeling features are the most valuable and without the need to write a code back with many different possibilities to choose from. And the second one is linked to the activity of the data preparation."
"We are creating models and putting them into production much faster than we would if we had just gone with a strict, code-based solution, like R or Python."
"It is a stable product."
"The interface is very good, and the algorithms are the very best."
"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"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."
"It doesn’t cost anything to use the product."
"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."
"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."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."
"It would be helpful if SPSS supported open-source features, for example, embedding R or Python scripts in SPSS Modeler."
"The product does not have a search function for tags."
"When you are not using the product, such as during the pandemic where we had worldwide lockdowns, you still have to pay for the licensing."
"The platform that you can deploy it on needs improvement because I think it is Windows only. I do not think it can run off a Red Hat, like the server products. I am pretty sure it is Windows and AIX only."
"Unstructured data is not appropriate for SPSS Modeler."
"It is very good, but slow. The slowness may be because we have not finalized all the background information in SPSS. It still needs some tweaking."
"Dimension reduction should be classified separately."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"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."
"Weka could be more stable."
"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."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"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."
"If there are a lot more lines of code, then we should use another language."
"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
IBM SPSS Modeler is ranked 4th in Data Mining with 38 reviews while Weka is ranked 2nd in Data Mining with 14 reviews. IBM SPSS Modeler is rated 8.0, while Weka is rated 7.6. The top reviewer of IBM SPSS Modeler writes "Easy to use, quick to learn, and offers many ways to analyze data". On the other hand, the top reviewer of Weka writes "Open source, good for basic data mining use cases except for the visualization results". IBM SPSS Modeler is most compared with Microsoft Power BI, KNIME, IBM SPSS Statistics, RapidMiner and Microsoft Azure Machine Learning Studio, whereas Weka is most compared with KNIME, IBM SPSS Statistics, Oracle Advanced Analytics, SAS Analytics and Splunk User Behavior Analytics. See our IBM SPSS Modeler vs. Weka report.
See our list of best Data Mining vendors.
We monitor all Data Mining reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.