£575.00

Price for this course

1 HOURS

Duration

Classroom IBM

Delivery

Available dates


Mon14Dec 20 TO Mon14Dec 20

Where

Tech Data
The Capitol Building, Oldbury
Bracknell
RG12 8FZ

Code

TR-646352
Mon14Dec 20 TO Mon14Dec 20

Where

Tech Data ILO UK
Connection details will be communicated separately
Instructor Led
Online

Code

TR-646353
Mon14Dec 20 TO Mon14Dec 20

Where

Tech Data
2nd Floor, Broadwall House, 21 Broadwall Street
London
SE1 9PL

Code

TR-646354
Mon08Mar 21 TO Mon08Mar 21

Where

Tech Data ILO UK
Connection details will be communicated separately
Instructor Led
Online

Code

TR-664256
Mon07Jun 21 TO Mon07Jun 21

Where

Tech Data ILO UK
Connection details will be communicated separately
Instructor Led
Online

Code

TR-664257

Overview

This course focuses on using analytical models to predict a categorical field, such as churn, fraud, response to a mailing, pass/fail exams, and machine break-down. Students are introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. Students will learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

Audience

• Analytics business users who have completed the Introduction to IBM SPSS Modeler and Data Mining course and who want to become familiar with analytical models to predict a categorical field (yes/no churn, yes/no fraud, yes/no response to a mailing, pass/fail exams, yes/no machine break-down, and so forth).

Prerequisites

• 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 a basic knowledge of modeling.
• Prior completion of Introduction to IBM SPSS Modeler and Data Science (v18.1) is recommended.

Objective

1: Introduction to predictive models for categorical targets
• Identify three modeling objectives
• Explain the concept of field measurement level and its implications for selecting a modeling technique
• List three types of models to predict categorical targets

2: Building decision trees interactively with CHAID
• Explain how CHAID grows decision trees
• Build a customized model with CHAID
• Evaluate a model by means of accuracy, risk, response and gain
• Use the model nugget to score records

3: Building decision trees interactively with C&R Tree and Quest
• Explain how C&R Tree grows a tree
• Explain how Quest grows a tree
• Build a customized model using C&R Tree and Quest
• List two differences between CHAID, C&R Tree, and Quest

4: Building decision trees directly
• Customize two options in the CHAID node
• Customize two options in the C&R Tree node
• Customize two options in the Quest node
• Customize two options in the C5.0 node
• Use the Analysis node and Evaluation node to evaluate and compare models
• List two differences between CHAID, C&R Tree, Quest, and C5.0

5: Using traditional statistical models
• Explain key concepts for Discriminant
• Customize one option in the Discriminant node
• Explain key concepts for Logistic
• Customize one option in the Logistic node

6: Using machine learning models
• Explain key concepts for Neural Net
• Customize one option in the Neural Net node

Course Outline

1: Introduction to predictive models for categorical targets
• Identify three modeling objectives
• Explain the concept of field measurement level and its implications for selecting a modeling technique
• List three types of models to predict categorical targets

2: Building decision trees interactively with CHAID
• Explain how CHAID grows decision trees
• Build a customized model with CHAID
• Evaluate a model by means of accuracy, risk, response and gain
• Use the model nugget to score records

3: Building decision trees interactively with C&R Tree and Quest
• Explain how C&R Tree grows a tree
• Explain how Quest grows a tree
• Build a customized model using C&R Tree and Quest
• List two differences between CHAID, C&R Tree, and Quest

4: Building decision trees directly
• Customize two options in the CHAID node
• Customize two options in the C&R Tree node
• Customize two options in the Quest node
• Customize two options in the C5.0 node
• Use the Analysis node and Evaluation node to evaluate and compare models
• List two differences between CHAID, C&R Tree, Quest, and C5.0

5: Using traditional statistical models
• Explain key concepts for Discriminant
• Customize one option in the Discriminant node
• Explain key concepts for Logistic
• Customize one option in the Logistic node

6: Using machine learning models
• Explain key concepts for Neural Net
• Customize one option in the Neural Net node



FAQs

What do I need to bring with me to my public class?

All required learning materials and equipment are provided in the classroom.

 

 

 

 

When do public training course fees have to be paid?

For public training classes payment must be received no later than three business days prior to the first day of class in order to remain in the class and confirm your seat. Failure to provide payment by this date may result in removal from the class, and/or late cancellation fees applied. You can submit payment in the form of a Purchase Order or credit card.

 

 

 

 

On-site (private) Course Pricing:

To find out more about On-site training e-mail us at enablement@agilesolutions.co.uk or call one of our offices.

 

 

 

 

What is the cancellation policy?

Requests for cancellations or date transfers need to be received at least ten (10) business days prior to the event start date in order to receive a full refund. If a cancellation or reschedule request is received less than ten (10) business days before the start date, the penalty of 100% of the cost of the course will be applied, resulting in no amount of the fee being refunded. Refunds will not be allowed for “no-shows” in our public training or IVA courses. This cancellation policy is strictly enforced.

 

 

 

 

What happens if Agile Solutions needs to cancel or reschedule a course?

Agile Solutions reserves the right to cancel events for any reason at any time. Cancellation liability for Agile Solutions, if Agile Solutions cancels the course, is limited to the return of course payment ONLY. Agile Solutions will not reimburse registrants for any other costs including but not limited to any travel cancellation fees or penalties, including airfare and hotel costs. PLEASE NOTE: If your registration status is either “Approved”, or “Pending Payment” you have not been confirmed for the class and it is recommended that you do not make any travel arrangements until you have received a confirmation e-mail letting you know the class and registration is confirmed.

 

 

 

 

How will I know if my course has been rescheduled?

Agile Solutions reserves the right to reschedule or cancel a course due to low enrollment or if necessitated by other circumstances. Agile Solutions will contact you via e-mail or phone to inform you of the change of schedule. Once you have been notified you may reschedule or receive a full credit. Agile Solutions shall not be liable for any other costs including but not limited to any non-refundable travel arrangements if a course is rescheduled or cancelled.