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."New algorithms are added into every version of Modeler, e.g., SMOTE, random forest, etc. The Derive node is used for the syntax code to derive the data."
"It is a great product for running statistical analysis."
"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 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."
"We had an IBM Guardium service contract where we used one of their resources to help us develop our prototype. It was a good experience, but they were helpful and responsive."
"It scales. I have not run into any challenges where it will not perform."
"We have a local representative who specializes in SPSS. He will help us do the PoC."
"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."
"It is a stable product."
"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."
"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."
"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's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"There are many options where you can fill all of the data pre-processing options that you can implement when you're importing the data. You can also normalize the data and standardize it in an easier way."
"The platform's cloud version needs improvements."
"I think mapping for geographic data would also be a really great thing to be able to use."
"Dimension reduction should be classified separately."
"When I used it in the office, back in the day, we did have some stability issues. Sometimes it just randomly crashed and we couldn't get good feedback. But when I use it for my own stuff now I don't have any problems."
"C&DS will not meet our scalability needs."
"Requires more development."
"I understand that it takes some time to incorporate some of the new algorithms that have come out in the last few months, in the literature. For example, there is an algorithm based on how ants search for food. And there are some algorithms that have now been developed to complement rules. So that's one of the things that we need to have incorporated into it."
"It would be beneficial if the tool would include more well-known machine learning algorithms."
"A few people said it became slow after a while."
"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."
"Weka is a little complicated and not necessarily suited for users who aren't skilled and experienced in data science."
"In terms of scalability, I think Weka is not prepared to handle a large number of users."
"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."
"Weka could be more stable."
"Not particularly user friendly."
"If you have one missing value in your dataset and this missing value belongs to a specific attribute and the attribute is a numeric attribute and there is only one missing data, whenever you import this data, the problem is that Weka cannot understand that this is a numeric field. It converts everything into a string, and there is no way to convert the string into numerical math. It's really very complicated."
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.