Random Forests & Uplift Models
Predictive Analytics
8 Videos | 37m 6s - 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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1.Random Forest Overview4m 46sUP NEXT
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2.Decision Tree Characteristics5m 41s
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3.Random Forest Model Error Measurement3m 39s
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4.Random Forest Model Concepts3m 3s
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5.Overview of Uplift Models5m
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6.Targeting with Uplift Models4m 34s
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7.How Uplift Models Work3m 45s
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8.Exercise: Advanced Predictive Tools3m 9s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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