I think the code modeling features are the most valuable and without the need to write a code back with many different possibilities to choose from. And the second one is linked to the activity of the data preparation.
Professor of Data Mining at Universidad Politecnica de Madrid
Real User
May 9, 2022
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.
Lecturer at School of Science, University of Phayao
Real User
May 24, 2018
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.
IBM SPSS Modeler is a robust tool that facilitates predictive modeling and data analysis through intuitive visual programming and customizable automation, enabling users to streamline data analytics processes with effectiveness.IBM SPSS Modeler combines ease of use with powerful functionalities, including statistical analysis and quick prototyping. Users can leverage visual programming and drag-and-drop features, making data exploration efficient. Its diverse algorithms and capability to...
Compared to other tools, the product works much easier to analyze data without coding.
We have full control of the data handling process.
I think the code modeling features are the most valuable and without the need to write a code back with many different possibilities to choose from. And the second one is linked to the activity of the data preparation.
In the solution, I like the virtualization of data flow since it shows what goes where, which is mostly the strength of the tool.
The quality is very good.
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's a very organized product. It's easy to use.
The supervised models are valuable. It is also very organized and easy to use.
You take two quarters and compare them and this tool is ideal because it gives you a lot of visibility on the before and after.
It is a great product for running statistical analysis.
Automation is great and this product is very organized.
Very good data aggregation.
It's very easy to use. The drag and drop feature makes it very easy when you are building and testing the streams. That's very useful.
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.
Automated modelling, classification, or clustering are very useful.