Description
At Sixe Engineering we have been providing official IBM training around the world for over 12 years. Get the best training from our specialists in Europe. We have important discounts and offers for two or more students.
Course details
| IBM course code: 0A038G | Category: IBM SPSS Modeler / IBM SPSS Modeler |
| Delivery: Online & on-site** | Course length in days: 1 |
Target audience
• Business Analysts
• Data Scientists
• Users of IBM SPSS Modeler responsible for building predictive models
Desired Prerequisites:
• 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.
Instructors
The great majority of the IBM courses we offer are taught directly by our engineers. This is the only way we can guarantee the highest quality. We complement all the training with our own materials and laboratories, based on our experience during the deployments, migrations and courses that we have carried out during all these years.
Added value
Our courses are deeply role oriented. To give an example, the needs for technology mastery are different for developer teams and for the people in charge of deploying and managing the underlying infrastructure. The level of previous experience is also important and we take it very seriously. That is why beyond (boring) commands and tasks, we focus on solving the problems that arise in the day to day of each team. Providing them with the knowledge, competencies and skills required for each project. In addition, our documentation is based on the latest version of each product.
Agenda and course syllabus
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
Do you need to adapt this syllabus to your needs? Are you interested in other courses? Ask us without obligation.
Locations for on-site delivery
- Austria: Vienna
- Belgium: Brussels, Ghent
- Denmark: Cophenhagen
- Estonia: Tallinn
- Finland: Helsinki
- France: Paris, Marseille, Lyon
- Germany: Berlin, Munich, Cologne, Hamburg
- Greece: Athens, Thessaloniki
- Italy: Rome
- Louxemburg: Louxembourg (city)
- Netherlands: Amsterdam
- Norway: Oslo
- Portugal: Lisbon, Braga, Porto, Coimbra
- Slovakia: Bratislava
- Slovenia: Bratislava
- Spain: Madrid, Sevilla, Valencia, Barcelona, Bilbao, Málaga
- Sweden: Stockholm
- Turkey: Ankara
- United Kingdom: London
