Course details

Random Forests & Uplift Models

Random Forests & Uplift Models


Overview/Description
Target Audience
Prerequisites
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/Description
Nestled within machine learning are ensemble techniques that enable the combination of multiple models to reduce prediction error and improve forecasting ability. Explore machine learning methods, including random forests and uplift models.

Target Audience
All individuals who are new to predictive analytics and wish to use it to optimize their business performance; business leaders; analysts; marketing, sales, software, and IT professionals who want to add predictive analytics to their skill set; and decision makers of any kind

Prerequisites
None

Expected Duration (hours)
0.7

Lesson Objectives

Random Forests & Uplift Models

  • 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
  • Course Number:
    df_prma_a16_it_enus

    Expertise Level
    Intermediate