Random Forests & Uplift Models

Predictive Analytics
  • 8 Videos | 37m 6s
  • Includes Assessment
  • Earns a Badge
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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

  • Playable
    1. 
    Random Forest Overview
    4m 46s
    UP NEXT
  • Playable
    2. 
    Decision Tree Characteristics
    5m 41s
  • Locked
    3. 
    Random Forest Model Error Measurement
    3m 39s
  • Locked
    4. 
    Random Forest Model Concepts
    3m 3s
  • Locked
    5. 
    Overview of Uplift Models
    5m
  • Locked
    6. 
    Targeting with Uplift Models
    4m 34s
  • Locked
    7. 
    How Uplift Models Work
    3m 45s
  • Locked
    8. 
    Exercise: Advanced Predictive Tools
    3m 9s

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