Descripción: Curso Predictive Modeling with IBM SPSS Modeler - SPVC
Formación en Business Analytics
This is the self paced training version of "Predictive Modeling with IBM SPSS Modeler" classroom course. This self paced training course demonstrates how to develop models to predict categorical and continuous outcomes, using such techniques as neural networks, decision trees, logistic regression, support vector machines, and Bayesian network models. Use of the binary classifier and numeric predictor nodes to automate model selection is included. Feature selection and detection of outliers are discussed. Expert options for each modeling node are reviewed in detail and advice is provided on when and how to use each model. You will also learn how to combine two or more models to improve prediction.
If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course.
This course follows either "Introduction to IBM SPSS Modeler and Data Mining" or Advanced Data Preparation with IBM SPSS Modeler is essential for anyone who wishes to become familiar with the full range of modeling techniques available in IBM SPSS Modeler to create predictive models.
You should have:
General computer literacy.
Experience using IBM SPSS Modeler (formerly Clementine), including
familiarity with the IBM SPSS Modeler environment,
reading in data files,
assessing data quality and handling missing data (including the type and data audit nodes),
basic data manipulation (including the derive and select nodes),
and creation of models.
Prior completion of Introduction to IBM SPSS Modeler and Data Mining is required and completion of Advanced Data Preparation with IBM SPSS Modeler is strongly encouraged.
An introductory course in statistics, or equivalent experience, would be helpful for the statistics-based modeling techniques.