Каталог курсов

Курсы IBM

Advanced Predictive Modeling Using IBM SPSS Modeler (v18.1.1)

 
Код курса  Код: 0A038GUA    Заявка  Предварительная запись    Продолжительность  Кол-во часов: 8 , Кол-во дней: 1


Course Description
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.

Objectives
Please refer to course overview

Audience
• Business Analysts
• Data Scientists
• Users of IBM SPSS Modeler responsible for building predictive models

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.

Topics
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 targetsCourse Description

This course builds on the courses Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18) and Predictive Modeling for Continuous Targets Using IBM SPSS Modeler (v18). It presents advanced techniques to predict categorical and continuous targets. Before reviewing the modeling techniques, data preparation issues are addressed such as partitioning and detecting anomalies. Also, a method to reduce the number of fields to a number of core fields, referred to as components or factors, is presented. Advanced classification models, such as Decision List, Support Vector Machines and Bayes Net, are reviewed. Methods are presented to combine individual models into a single model in order to improve predictive power, including running and evaluating many models in a single run, both for categorical and continuous targets.


 Objectives

Please refer to course overview.

 Audience

Users of IBM SPSS Modeler responsible for building predictive models who want to leverage the full potential of classification models in IBM SPSS Modeler.

 Prerequisites

• General computer literacy

• Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and basic knowledge of modeling.

• Prior completion of Introduction to Predictive Models using IBM SPSS Modeler (v18) is recommended.

• 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.

 Topics

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 basic ideas behind PCA/Factor

•  Customize two options in the PCA/Factor node

3. Using Decision List to Create Rulesets

•  Explain how Decision List builds a ruleset

•  Use Decision List interactively

•  Create rulesets directly with Decision List

4. Exploring advanced predictive models

•  Explain the basic ideas behind SVM

•  Customize two options in the SVM node

•  Explain the basic ideas behind Bayes Net

•  Customize two options in the SVM node

5. Combining Models

•  Use the Ensemble node to combine model predictions

•  Improve the model performance by meta-level modeling

6. Finding the Best Predictive Model

•  Find the best model for categorical targets with AutoClassifier

•  Find the best model for continuous targets with AutoNumeric


Ссылка курса обучения
Назад в раздел





Для предварительной записи на курсы или
уточнения информации позвоните по телефонам:
(044) 492-29-29, 594-98-98
e-mail: training@muk.com.ua

Или заполните заявку online.

Свежие новости

14.11.2019 MUK webinar | «Микросервисная архитектура: принципы декомпозиции монолитных приложений», 27.11.2019

08.11.2019 MUK webinar | Red hat OpenStack Platform – технический обзор
Учебный центр - Red Hat Certified Training Partner – объявляет набор на бесплатный вебинар по техническому обзору Red hat OpenStack Platform

28.10.2019 Расписание Учебного центра МУК 2020 уже доступно!
Расписание Учебного центра МУК 2020 уже доступно!

10.10.2019 Сертифицированный тренер Fortinet – тренер Учебного центра МУК
Сертифицированный тренер Fortinet – тренер Учебного центра МУК