SAS
Four Rare Machine Learning Skills All Data Scientists Need
SAS

Four Rare Machine Learning Skills All Data Scientists Need

Eric Siegel

Instructor: Eric Siegel

Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

5 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

5 hours to complete
3 weeks at 1 hour a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Uplift modeling (aka persuasion modeling)

  • Major pitfalls: the accuracy fallacy and p-hacking

  • The paradox of ensemble models

Details to know

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Assessments

12 assignments

Taught in English

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There is 1 module in this course

This one-week course has only one module, which covers the course's four rare yet vital topics: (1) UPLIFT MODELING: How do you optimize marketing – which is meant to persuade – if we cannot generally establish causal relationships? Put another way, how do you model and predict influence when you cannot measure influence? The special, advanced method uplift modeling (aka persuasion modeling) goes beyond predicting an outcome to actually predicting the influence that a treatment decision would have on that outcome. We'll explore the marketing applications of uplift modeling and see success stories from the likes of US Bank and President Obama's 2012 reelection campaign. (2) THE ACCURACY FALLACY: For many machine learning projects, high accuracy is unattainable – and, besides, accuracy isn't the right metric in the first place. But many projects are falsely advertised as "highly accurate." Learn to identify occurrences of the accuracy fallacy, a common misstep by which researches spread misinformation about predictive model performance. (3) P-HACKING: In what way is bigger data more dangerous? How do we avoid being fooled by random noise and ensure scientific discoveries are trustworthy? This prevalent pitfall is a huge gotcha! (4) THE PARADOX OF ENSEMBLE MODELS: Is there a way to advance model capability and performance that's elegant and simple, without involving the complexity of neural networks? Why yes there is.

What's included

14 videos6 readings12 assignments8 discussion prompts

Instructor

Eric Siegel
SAS
5 Courses16,316 learners

Offered by

SAS

Recommended if you're interested in Machine Learning

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