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."The supervised models are valuable. It is also very organized and easy to use."
"A lot of jobs that are stuck in Excel due to the huge numbers of rows are tackled pretty quickly."
"We are using it either for workforce deployment or to improve our operations."
"Very good data aggregation."
"It continues to be a very flexible platform, so that it handles R and Python and other types of technology. It seems to be growing with additional open-source movement out there on different platforms."
"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."
"Some basic form of feature engineering for classification models. This really quickens the model development process."
"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."
"The interface is very good, and the algorithms are the very best."
"It doesn’t cost anything to use the product."
"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 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."
"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."
"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 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 think mapping for geographic data would also be a really great thing to be able to use."
"The forecasting could be a bit easier."
"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."
"Requires more development."
"It is not integrated with Qlik, Tableau, and Power BI."
"Customer support is hard to contact."
"We have run into a few problems doing some entity matching/analytics."
"The platform's cloud version needs improvements."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"Weka could be more stable."
"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."
"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
"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."
"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."
"Not particularly user friendly."
"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."
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 IBM Watson 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.
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