We performed a comparison between IBM Predictive Analytics and RapidMiner based on real PeerSpot user reviews.
Find out what your peers are saying about Alteryx, RapidMiner, SAP and others in Predictive Analytics."The most valuable feature is the predictive capability in marketing use cases."
"Scalability is not really a concern with RapidMiner. It scales very well and can be used in global implementations."
"Using the GUI, I can have models and algorithms drag and drop nodes."
"The most valuable feature of RapidMiner is that it is code free. It is similar to playing with Lego pieces and executing after you are finished to see the results. Additionally, it is easy to use and has interesting utilities when preparing the data. It has a utility to automatically launch a series of models and show the comparisons. When finished with the comparisons you can select the best one, and deploy it automatically."
"The most valuable feature of RapidMiner is that it can read a large number of file formats including CSV, Excel, and in particular, SPSS."
"The solution is stable."
"It's helpful if you want to make informed decisions using data. We can take the information, tease out the attributes, and label everything. It's suitable for profiling and forecasting in any industry."
"The most valuable features are the Binary classification and Auto Model."
"The GUI capabilities of the solution are excellent. Their Auto ML model provides for even non-coder data scientists to deploy a model."
"Using IBM Predictive Analytics requires more skill, resources, and training than some other solutions."
"A great product but confusing in some way with regard to the user interface and integration with other tools."
"Improve the online data services."
"I would appreciate improvements in automation and customization options to further streamline processes."
"The server product has been getting updated and continues to be better each release. When I started using RapidMiner, it was solid but not easy to set up and upgrade."
"I think that they should make deep learning models easier."
"The price of this solution should be improved."
"I would like to see more integration capabilities."
"The biggest problem, not from a platform process, but from an avoidance process, is when you work in a heavily regulated environment, like banking and finance. Whenever you make a decision or there is an output, you need to bill it as an avoidance to the investigator or to the bank audit team. If you made decisions within this machine learning model, you need to explain why you did so. It would better if you could explain your decision in terms of delivery. However, this is an issue with all ML platforms. Many companies are working heavily in this area to help figure out how to make it more explainable to the business team or the regulator."
Earn 20 points
IBM Predictive Analytics is ranked 22nd in Predictive Analytics while RapidMiner is ranked 2nd in Predictive Analytics with 19 reviews. IBM Predictive Analytics is rated 7.0, while RapidMiner is rated 8.6. The top reviewer of IBM Predictive Analytics writes "Good prediction capability for marketing purposes, although it needs to be more flexible". On the other hand, the top reviewer of RapidMiner writes "Offers good tutorials that make it easy to learn and use, with a powerful feature to compare machine learning algorithms". IBM Predictive Analytics is most compared with , whereas RapidMiner is most compared with KNIME, Alteryx, Dataiku Data Science Studio, Tableau and Microsoft Azure Machine Learning Studio.
See our list of best Predictive Analytics vendors.
We monitor all Predictive Analytics 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.
