Random Forests & Uplift Models

Predictive Analytics    |    Intermediate
  • 8 videos | 33m 36s
  • Includes Assessment
  • Earns a Badge
Rating 4.5 of 12 users Rating 4.5 of 12 users (12)
Nestled within machine learning are ensemble techniques, enabling the combination of multiple models to reduce prediction error and improve forecasting ability. Examine machine learning methods, including random forests and uplift models.

WHAT YOU WILL LEARN

  • Identify key features of random forests
    Identify key features of decision trees
    Recognize random forest performance measurements
    Identify key random forest model concepts
  • Identify key features of uplift models
    Recognize who to target with uplift models
    Recognize how uplift models work
    Implement a random forest and an uplift model using an example dataset

IN THIS COURSE

  • 4m 46s
    Learn how to identify key features of random forests. FREE ACCESS
  • 5m 41s
    To identify key features of decision trees, find out how. FREE ACCESS
  • Locked
    3.  Random Forest Model Error Measurement
    3m 39s
    Upon completion of this video, you will be able to recognize random forest performance measurements. FREE ACCESS
  • Locked
    4.  Random Forest Model Concepts
    3m 3s
    In this video, you will learn how to identify key random forest model concepts. FREE ACCESS
  • Locked
    5.  Overview of Uplift Models
    5m
    In this video, learn how to identify key features of uplift models. FREE ACCESS
  • Locked
    6.  Targeting with Uplift Models
    4m 34s
    Upon completion of this video, you will be able to recognize who to target with uplift models. FREE ACCESS
  • Locked
    7.  How Uplift Models Work
    3m 45s
    After completing this video, you will be able to recognize how uplift models work. FREE ACCESS
  • Locked
    8.  Exercise: Advanced Predictive Tools
    3m 9s
    Find out how to implement a random forest and an uplift model using an example dataset. FREE ACCESS

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