"The technical support is very good."
"I found the ease of use of the solution the most valuable. Additionally, other valuable features include: the user interface, power to extract data, compatibility with other technologies (specifically with PS400), and automation of several tasks."
"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 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."
"It doesn’t cost anything to use the product."
"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."
"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 mainly use this solution for the regression tree, and for its association rules. I run these two methodologies for Weka."
"The initial setup is challenging if doing it for the first time."
"The solution is much more complex than other options."
"A few people said it became slow after a while."
"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."
"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."
"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."
"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."
"Not particularly user friendly."
"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."
SAS Enterprise Miner is ranked 8th in Data Mining with 2 reviews while Weka is ranked 4th in Data Mining with 8 reviews. SAS Enterprise Miner is rated 6.6, while Weka is rated 7.6. The top reviewer of SAS Enterprise Miner writes "Good technical support but too complex and not open-source". On the other hand, the top reviewer of Weka writes "Relatively stable with excellent accuracy and there's no need to know coding". SAS Enterprise Miner is most compared with SAS Visual Analytics, IBM SPSS Modeler, Microsoft Azure Machine Learning Studio, RapidMiner and Databricks, whereas Weka is most compared with KNIME, IBM SPSS Statistics, IBM SPSS Modeler, SAS Analytics and Elastic Enterprise Search. See our SAS Enterprise Miner vs. Weka report.
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