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IBM SPSS Modeler is a data mining and text analytics software application from IBM.It is used to build predictive models and conduct other analytic tasks. It has a visual interface which allows users to leverage statistical and data mining algorithms without programming.
SPSS is a widely used program for statistical analysis in social science. [7] It is also used by market researchers, health researchers, survey companies, government, education researchers, industries, marketing organizations, data miners, [8] and others.
There are a few reviews of free statistical software. There were two reviews in journals (but not peer reviewed), one by Zhu and Kuljaca [26] and another article by Grant that included mainly a brief review of R. [27] Zhu and Kuljaca outlined some useful characteristics of software, such as ease of use, having a number of statistical procedures and ability to develop new procedures.
It makes some of the old CRISP-DM documents available for download and it has incorporated it into its SPSS Modeler product. [6] Based on current research, CRISP-DM is the most widely used form of data-mining model because of its various advantages which solved the existing problems in the data mining industries.
SPSS Modeler: data mining software provided by IBM. STATISTICA Data Miner: data mining software provided by StatSoft. Tanagra: Visualisation-oriented data mining software, also for teaching. Vertica: data mining software provided by Hewlett-Packard. Google Cloud Platform: automated custom ML models managed by Google.
SPSS Inc. was a software house headquartered in Chicago and incorporated in Delaware, most noted for the proprietary software of the same name SPSS. The company was started in 1968 when Norman Nie , Dale Bent, and Hadlai "Tex" Hull developed and started selling the SPSS software.
SPSS Modeler – Commercial software that includes an implementation for Bayesian networks. Stan (software) – Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler (NUTS), [ 27 ] a variant of Hamiltonian Monte Carlo.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
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