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 visual modeling capability is one of its attractive features."
"We have been able to do some predictive modeling with it"
"The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well."
"It is a great product for running statistical analysis."
"Extremely easy to use, it offers a generous selection of proprietary machine learning algorithms."
"It gives you a GUI interface, which is a lot more user-friendly and easier to use compared to writing R scripts or Python."
"It handles large data better than the previous system that we were using, which was basically Excel and Access. We serve upwards of 300,000 parts over a 150 regions and we need to crunch a lot of numbers."
"It makes pretty good use of memory. There are algorithms take a long time to run in R, and somehow they run more efficiently in Modeler."
"The interface is very good, and the algorithms are the very best."
"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."
"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's best features are its user-friendly graphic interface interpretation of data sets and the ease of analyzing data."
"I mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"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."
"In Weka, anyone can access the program without being a programmer, which is a good feature since the entry cost is very low."
"Regarding visual modeling, it is not the biggest strength of the product, although from what I hear in the latest release it's going to be a lot stronger. That, to me, has always been the biggest flaw in using this. It's very difficult to get good visualization."
"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."
"Requires more development."
"I would like better integration into the Weather Company solution. I have raised a couple of concerns about this integration and having more time series capabilities."
"We have run into a few problems doing some entity matching/analytics."
"Formula writing is not straightforward for an Excel user. Totally new set of functions, which takes time to learn and teach."
"It would be helpful if SPSS supported open-source features, for example, embedding R or Python scripts in SPSS Modeler."
"The challenge for the very technical data scientists: It is constraining for them."
"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."
"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."
"If there are a lot more lines of code, then we should use another language."
"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."
"Not particularly user friendly."
"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."
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 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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