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Advanced Predictive Modeling Using IBM SPSS Modeler (v18.1.1)
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. The participant is 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.
- Preparing data for modeling
- Reducing data with PCA/Factor
- Creating rulesets for flag targets with Decision List
- Exploring advanced supervised models
- Combining models
- Finding the best supervised model
• 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.
1: Preparing data for modeling
• Address general data quality issues
• Handle anomalies
• Select important predictors
• Partition the data to better evaluate models
• Balance the data to build better models
2: Reducing data with PCA/Factor
• Explain the idea behind PCA/Factor
• Determine the number of components/factors
• Explain the principle of rotating a solution
3: Creating rulesets for flag targets with Decision List
• Explain how Decision List builds a ruleset
• Use Decision List interactively
• Create rulesets directly with Decision List
4: Exploring advanced supervised models
• Explain the principles of Support Vector Machine (SVM)
• Explain the principles of Random Trees
• Explain the principles of XGBoost
5: Combining models
• Use the Ensemble node to combine model predictions
• Improve model performance by meta-level modeling
6: Finding the best supervised model
• Use the Auto Classifier node to find the best model for categorical targets
• Use the Auto Numeric node to find the best model for continuous targets
When you complete the Instructor-Led version of this course, you will be eligible to earn an IBM Training Badge that can be displayed on your website, business cards, and social media channels to demonstrate your mastery of the skills you learned here.Learn more about our IBM SPSS Modeler Badge Program →
- Duration: 16 Hours
- Delivery Format: Classroom Training, Online Training
- Price: 1,630.00 USD
- Duration: 16 Hours
- Delivery Format: Self-Paced Training
- Price: 825.00 USD
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
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Self-Paced Training Terms & Conditions
THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 30 DAYS TO COMPLETE THE COURSE.
This is a Self-Paced virtual class; it is intended for students who do not need the support of a classroom instructor. If you feel you would better benefit from having access to a Subject Matter Expert, please enroll in the Instructor-Led version instead. Minimal technical support is provided to address issues with accessing the platform or problems within the lab environment.
Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.
You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
If the course requires a remote lab system, the lab system access is allocated on a first-come, first-served basis. When you are not using the elab system, ensure that you suspend your elab to maximize your hours available to use the elab system.
Click the Skytap Connectivity Test button to ensure this computer's hardware, software and internet connection works with the SPVC Lab Environment.
Click the Skytap Connectivity Documentation button to read about the hardware, software and internet connection requirements.
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