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Our IBM SPSS Modeler training courses are offered in a variety of delivery options such as classroom, self-paced training, and instructor-led virtual classes, all aimed at helping you and your team get the training that you need. Need some help?
Contact us for assistance. Need help finding which course is best for you? Please submit a question, along with your name and email and one of our representatives will reach out to help. Use promo code Virtual30 when registering. LearnQuest has Virtual and Self-Paced options available.
To view these options, please click the boxes under delivery method to filter. This course covers advanced topics to aid in the preparation of data for a successful data science project.
You will learn. Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their. The participant is first introduced to a technique named.
This course presents advanced models to predict categorical and continuous targets. Before reviewing the models, data preparation. Contains: PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at. Data science without a Ph. This course focuses on reviewing concepts of data science, where participants will learn the stages of a data science project.
Data Science without a Ph. The principles. X eLearning. This course reviews the basics of how to import, explore,. This course reviews the basics of how to import, explore, and prepare. This course provides an introduction to supervised models, unsupervised models, and association models.
This is an application-oriented. This course gets you up and running with a set of procedures for analyzing time series data. Learn how to forecast. This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing,.
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Creating a Decision Tree Analysis using SPSS Modeler
SPSS Modeler is statistical analysis software used for data analysis, data mining and forecasting. Statistical analysis allows us to use a sample of data to make predictions about a larger population. Creating predictive models utilizing the information currently at your fingertips to predict what decisions will impact your future success. Predictive analytics is hugely important as it allows you to see into the future and make quality decisions based on long term planning. Decision tree analyses are popular models because they indicate which predictors are most strongly related to the target. The purpose of decision trees is to model a series of events and look at how it affects an outcome. This type of model calculates a set of conditional probabilities based on different scenarios.
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. One of its main aims from the outset was to get rid of unnecessary complexity in data transformations, and to make complex predictive models very easy to use. The first version incorporated decision trees ID3 , and neural networks backprop , which could both be trained without underlying knowledge of how those techniques worked. This name continued for a while after SPSS's acquisition of the product.