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."I think it is the point and drag features that are the most valuable. You can simply click at the windows, and then pull up the functions."
"Some basic form of feature engineering for classification models. This really quickens the model development process."
"We have a local representative who specializes in SPSS. He will help us do the PoC."
"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."
"We have been able to do some predictive modeling with it"
"It's very easy to use. The drag and drop feature makes it very easy when you are building and testing the streams. That's very useful."
"Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms."
"The visual modeling capability is one of its attractive features."
"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"It is a stable product."
"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."
"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."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"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."
"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."
"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."
"We would like to see better visualizations and easier integration with Cognos Analytics for reporting."
"I would like see more programming languages added, like MATLAB. That would be better."
"The biggest issue with the visual modeling capability is that we can't extract the SQL code under the hood."
"Dimension reduction should be classified separately."
"If IBM could add some of the popular models into the SPSS for further analysis, like popular regression models, I think that would be a helpful improvement."
"Initial setup of the software was complex, because of our own problems within the government."
"A few people said it became slow after a while."
"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."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"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."
"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."
"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."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
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 KNIME, Microsoft Power BI, RapidMiner, IBM SPSS Statistics and Databricks, whereas Weka is most compared with KNIME, IBM SPSS Statistics, Oracle Advanced Analytics, Splunk User Behavior Analytics and SAS Analytics. See our IBM SPSS Modeler vs. Weka report.
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