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."IBM was chosen because of usability. It's point and click, whereas the other out-of-the box-solution, or open-source solutions, require full-on programming and a much higher skill level."
"Our go live process has been slightly enhanced compared to the previous programmatic process. There is now a faster time to production from the business end."
"Very good data aggregation."
"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"
"Our business units' capabilities with SPSS Modeler is high. They no longer waste time on modeling and algorithms, meaning they are not coding any more. For example, segmentation projects now take one to three months, rather than six months to a year, as before."
"You take two quarters and compare them and this tool is ideal because it gives you a lot of visibility on the before and after."
"Some basic form of feature engineering for classification models. This really quickens the model development process."
"We have full control of the data handling process."
"The interface is very good, and the algorithms are the very best."
"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."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"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."
"Weka's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
"It is a stable product."
"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."
"It doesn’t cost anything to use the product."
"It would be beneficial if the tool would include more well-known machine learning algorithms."
"The challenge for the very technical data scientists: It is constraining for them."
"The product does not have a search function for tags."
"The standard package (personal) is not supported for database connection."
"C&DS will not meet our scalability needs."
"I would not rate the technical support very well. The technicians have accents. When you do find someone, it is very hard to get somebody able to answer the technical questions."
"Neural networks are quite simple, and now neural networks are evolving to these architecture related to deep learning, etc. They didn't incorporate this in IBM SPSS Modeler."
"The platform's cloud version needs improvements."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"Weka could be more stable."
"A few people said it became slow after a while."
"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."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"Not particularly user friendly."
"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."
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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