Random Forests & Uplift Models
Predictive Analytics
| Intermediate
- 8 videos | 33m 36s
- Includes Assessment
- Earns a Badge
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
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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
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4m 46sLearn how to identify key features of random forests. FREE ACCESS
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5m 41sTo identify key features of decision trees, find out how. FREE ACCESS
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3. Random Forest Model Error Measurement3m 39sUpon completion of this video, you will be able to recognize random forest performance measurements. FREE ACCESS
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4. Random Forest Model Concepts3m 3sIn this video, you will learn how to identify key random forest model concepts. FREE ACCESS
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5. Overview of Uplift Models5mIn this video, learn how to identify key features of uplift models. FREE ACCESS
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6. Targeting with Uplift Models4m 34sUpon completion of this video, you will be able to recognize who to target with uplift models. FREE ACCESS
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7. How Uplift Models Work3m 45sAfter completing this video, you will be able to recognize how uplift models work. FREE ACCESS
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8. Exercise: Advanced Predictive Tools3m 9sFind out how to implement a random forest and an uplift model using an example dataset. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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