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Advanced Predictive Modeling Using IBM SPSS Modeler (v18.2)
Code:
0A039G
Duration:
1 Day
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$955
USD
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This course presents advanced models to predict categorical and continuous targets. Before reviewing the models, data preparation issues are addressed such as partitioning, detecting anomalies, and balancing data. You are first introduced to a technique named PCA/Factor, to reduce the number of fields to a number of core fields, referred to as components or factors. The next units focus on supervised models, including Decision List, Support Vector Machines, Random Trees, and XGBoost. Methods are reviewed to combine supervised models and execute them in a single run, both for categorical and continuous targets.
This course is available in the following formats:
Duration: 1 Day
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Delivery Format: Virtual Classroom
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$ 955 |
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Delivery Format: Virtual Classroom
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$ 955 |
- How to prepare data for modeling
- Reducing data with PCA/Factor
- Using Decision List to create rulesets for flag targets
- Advanced supervised models
- Combining models
- Establishing supervised models
- Business Analysts
- Data Scientists
- Users of IBM SPSS Modeler responsible for building predictive models
- Familiarity with the IBM SPSS Modeler environment (creating, editing, opening, and saving streams)
- Familiarity with basic modeling techniques, either through completion of the courses Predictive Modeling for Categorical Targets Using IBM SPSS Modeler and/or Predictive Modeling for Continuous Targets Using IBM SPSS Modeler, or by experience with predictive models in IBM SPSS Modeler