Discover the challenges of ensembles during the scoring phase.
- [Instructor] So what about scoring ensemble models?…Ensembles are really hot right now…so we'll want to talk a bit about the implications…of choosing an ensemble when it gets to scoring.…What ensembles are is a collection of models.…It could be just a handful.…It could be as many as 40 or 50.…They come in all kinds of different styles.…But remember that the way that this is going to work…is you have to build the individual models…and then you have to combine them in some way.…And sometimes you combine them by simply…performing an average.…
Or another option has been doing especially well…in data mining competitions recently,…and it's called stacking.…You build multiple models, and then there's…another model on top, which takes the predictions…of the first models as input,…and then produces your final prediction.…They're really something, and they perform very well.…That's why they're always among the top performers…in these predictive analytics competitions.…But a bit of a cautionary note.…Clearly, if you're building five models,…
Note: 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
Machine Learning and AI Foundations: Recommendationswith Adam Geitgey58m 7s Intermediate
Deploying Scalable Machine Learning for Data Sciencewith Dan Sullivan1h 43m Intermediate
Defining terms1m 48s
1. The Phases of a Machine Learning Project
2. Designing a Machine Learning Dataset
3. Data Prep Challenges
4. Modeling Challenges
7. Monitoring and Maintenance
Next steps1m 1s
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