Discover why balancing is important and how to do it properly in this video.
- [Instructor] What do you do if you're analyzing something…that is highly out of balance?…For instance, it's extremely unlikely that fraud…represents half of our insurance claims.…We certainly hope not.…So perhaps you've got 20,000 fraudulent claims,…but over the same period,…two million legitimate claims.…The concept of balancing…is forcing the numbers to be in balance…by discarding at random some of the common cases.…
In this instance, legitimate claims are more common…so we discard some of them.…I get into the theory of this in more depth…in my introduction to classification course.…For now, trust in the fact…that the modeler does this fairly often…and they know how to do it…and they do it because it works.…For now, let's attend to the following fact.…We're suddenly down to just 40,000 cases…and we're only going to have half of those cases…in our training data set.…
But don't you want to emphasize…training numbers over testing numbers?…Yes, that would indeed make sense to do.…So of course with a million frauds…
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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