IBM Smart Analytics vs Weka comparison

"Log Analytics (LA) allows a user to see patterns of behavior and isolate issues quickly, without the need to manually access individual systems and parse logs manually."

"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 easy to use Data Mining solution, great for learning and for doing small experiments before exploring the data deeper, with a large number and diversity of algorithms that make it an excellent solution for rapid testing."

"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 is a stable product."

"The interface is very good, and the algorithms are the very best."

"Working with complicated algorithms in huge datasets is really easy in Weka."

"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."

"The indexing engine (proprietary build of LogStash) is well... very LogStash'ish... It requires more work to normalize the log feeds than competing products."

"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."

"While it might offer insights for basic warehouse tasks, it falls short of deeper understanding and results."

"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."

"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."

"Scalability and performance are the main aspect of improvement in Weka, since it has the main Java limitations, regarding the JVM."

"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."

"Currently, I am using an open-source version so I don't know much about the price of this solution."

"The solution is free and open-source."

"As far as I know, Weka is a freeware tool, and I am not aware if they have an online solution or if it is a commercial product."

"We use the free version now. My faculty is very small."