Advanced Predictive Modeling Using IBM SPSS Modeler (V18.1.1) Schulung
Advanced techniques to predict categorical and continuous targets
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.
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Inhalte SPSS Predictive Modeler IBM Schulung
Please refer to course overview
Course Outline
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
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Diese SPSS Predictive Modeler IBM Schulung führen wir auch bei dir im Unternehmen als individualisierte SPSS Predictive Modeler IBM-Firmenschulung durch.
Entscheidungshelfer
SPSS Predictive Modeler IBM Schulung
Zielgruppe
Business Analysts
Data Scientists
Users of IBM SPSS Modeler responsible for building predictive models
Voraussetzungen
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.
4.8
(112)
Advanced Predictive Modeling Using IBM SPSS Modeler (V18.1.1) Schulung
Advanced techniques to predict categorical and continuous targets
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.
Advanced Predictive Modeling Using IBM SPSS Modeler (V18.1.1) Schulung
