Explore some reasons why some algorithms are much slower than others with a focus on ensemble models.
- [Narrator] The third reason why…some algorithmic approaches are going to be slower…is we literally build more models in some cases.…So for instance, going all the way back to the 80's…it was proposed that we could build…more accurate decision trees if we simply built a bunch…of them and then averaged.…That's called bagging, but over the years a number…of new algorithms have been developed…that capitalize in this same idea.…Somewhat famously there's one called random forests.…
More recently, there's a fancier one called XGBoost,…but all of these do that same basic notion.…They build lots of trees and then average them.…So think about this now.…If you're going to build 50 trees and average them,…you know that your computational time…is going to be about 50 times more.…That can start to catch up with you.…There's a completely different sense…in which we could talk about building more models.…Let me give you an example.…Years ago I was running an analytics team…and one of the big projects that we did…was building models for each SKU so each bike model…
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Released
12/11/2018Note: This course is software agnostic. The emphasis is on strategy and planning. Examples, calculations, and software results shown are for training purposes only.
- Evaluating the proper amount of data
- Assessing data quality and quantity
- Seasonality and time alignment
- Data preparation challenges
- Data modeling challenges
- Scoring machine-learning models
- Deploying models and adjusting data prep and scoring
- Monitoring and maintenance
Skill Level Beginner
Duration
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Introduction
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Defining terms1m 48s
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1. The Phases of a Machine Learning Project
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2. Designing a Machine Learning Dataset
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How much data do I need?2m 12s
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Balancing1m 56s
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Who truly has big data?3m 43s
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Assessing data3m 32s
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3. Data Prep Challenges
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Data and the data scientist2m 55s
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Dummy coding2m 1s
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Feature engineering2m 51s
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4. Modeling Challenges
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Slow algorithms: Brute force1m 59s
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Slow algorithms: More models2m 24s
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How to sample properly2m 36s
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Modeling with missing data3m 37s
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5. Scoring
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Scoring a black box model2m 50s
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Scoring an ensemble1m 49s
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6. Deployment
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Batch vs. real-time scoring4m 39s
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Data prep and scoring2m 59s
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7. Monitoring and Maintenance
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Conclusion
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Next steps1m 1s
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Video: Slow algorithms: More models