Mike TurekVice President, Business Analysis & Performance at Starboard Cruise Services
We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
"The most valuable feature is the ability to handle large data sets."
"The technical support is okay."
"It's very easy to use once you learn it."
"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."
"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."
"Working with complicated algorithms in huge datasets is really easy in Weka."
"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 mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"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."
"This solution should be made more user-friendly."
"One of the things that can be simplified is self-service analytics, especially for a citizen developer or a citizen data scientist."
"The installation could also be easier, and the price could be better."
"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."
"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."
"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."
"Within the basic Weka tool, I don't see many tools that are available where we can analyze and visualize the data that well."
"If there are a lot more lines of code, then we should use another language."
"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."
SAS Analytics is ranked 6th in Data Mining with 3 reviews while Weka is ranked 5th in Data Mining with 7 reviews. SAS Analytics is rated 9.0, while Weka is rated 7.2. The top reviewer of SAS Analytics writes "A user-friendly, easy coding analytics solution that is good for typical predictive analytics". On the other hand, the top reviewer of Weka writes "Relatively stable with excellent accuracy and there's no need to know coding". SAS Analytics is most compared with KNIME, IBM SPSS Modeler, Oracle Advanced Analytics, IBM Watson Explorer and SAS Enterprise Miner, whereas Weka is most compared with KNIME, IBM SPSS Statistics, IBM SPSS Modeler, SAS Enterprise Miner and ELK Elasticsearch. See our SAS Analytics vs. Weka report.
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