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Partitioned Models

Learn how to train a machine learning model on partitions of your dataset.

About this course

 

Partitioned Models demonstrates how to build a partitioned model trained on subgroups, or partitions, of the dataset, and compare the results with a non-partitioned model trained on the whole dataset.

 

In the hands-on lesson, we will work with flight data. We will predict the arrival delay time for a given flight based on the flight's characteristics, such as departure delay time. Before the hands-on section, you will have a chance to grasp the concept of partitioned models by watching short videos.

 

 

 

Learning Objectives

At the end of this course, you will be able to:

1 - Describe partitioned models to a colleague

2 - Create a partitioned model from a partitioned dataset in Dataiku DSS

3 - Compare results from a non-partitioned model with the partitioned model

 

Course Properties

Course Title Partitioned Models

Target Audience

Anyone who wants to learn how to create partitioned models in Dataiku DSS

Access Level

Free / included with registration

Estimated Time for Completion

24 Minutes

Completion Criteria

Pass the course checkpoint with 80%

Supplemental Materials (Y/N)

NONE

Knowledge Prerequisite(s)

NONE

Technical Prerequisite(s)

Dataiku DSS - Latest version (FREE EDITION is enough)

 

Start by watching the Partitioned Models course overview video.

Curriculum24 min

  • Preview
    Course Introduction: Partitioned Models 0 min
  • Create a Partitioned Model
  • Concept: Partitioning 3 min

    Concept: Partitioning

  • Concept: Partitioned Models 5 min
  • Concept Summary: Partitioned Models 2 min
  • Hands-On: Partitioned Models 5 min
  • Wrap Up
  • Course Checkpoint 6 min

    Course Checkpoint

  • Tell Us What You Think 1 min
  • Course Complete 1 min

    Course Complete

About this course

 

Partitioned Models demonstrates how to build a partitioned model trained on subgroups, or partitions, of the dataset, and compare the results with a non-partitioned model trained on the whole dataset.

 

In the hands-on lesson, we will work with flight data. We will predict the arrival delay time for a given flight based on the flight's characteristics, such as departure delay time. Before the hands-on section, you will have a chance to grasp the concept of partitioned models by watching short videos.

 

 

 

Learning Objectives

At the end of this course, you will be able to:

1 - Describe partitioned models to a colleague

2 - Create a partitioned model from a partitioned dataset in Dataiku DSS

3 - Compare results from a non-partitioned model with the partitioned model

 

Course Properties

Course Title Partitioned Models

Target Audience

Anyone who wants to learn how to create partitioned models in Dataiku DSS

Access Level

Free / included with registration

Estimated Time for Completion

24 Minutes

Completion Criteria

Pass the course checkpoint with 80%

Supplemental Materials (Y/N)

NONE

Knowledge Prerequisite(s)

NONE

Technical Prerequisite(s)

Dataiku DSS - Latest version (FREE EDITION is enough)

 

Start by watching the Partitioned Models course overview video.

Curriculum24 min

  • Preview
    Course Introduction: Partitioned Models 0 min
  • Create a Partitioned Model
  • Concept: Partitioning 3 min

    Concept: Partitioning

  • Concept: Partitioned Models 5 min
  • Concept Summary: Partitioned Models 2 min
  • Hands-On: Partitioned Models 5 min
  • Wrap Up
  • Course Checkpoint 6 min

    Course Checkpoint

  • Tell Us What You Think 1 min
  • Course Complete 1 min

    Course Complete