Chapter 2-7 (Chapter 2: Automated Machine Learning (What is it? …
Chapter 2: Automated Machine Learning
machine learning life cycle
define project objectives -> acquire and explore data -> model data -> interpret and communicate -> implement, document, and maintain
What is it?
"off the shelf methods that can be used easily in the field, without machine learning knowledge"
exploratory data analysis
algorith selection and hyper-parameter tuning
What it is not
not automatic ML
Tools and Platforms
8 criteria for excellence
accuracy, productivity, ease of use, understanding and learning, resource availability, process transparency, generalization, recommend actions
Chapter 7: Success, Risk, Continuation
ID success criteria
Who will use the model?
Is management on board with the project?
Can the model drivers be visualized?
How much value can the model produce?
decide whether to continue
Chapter 3: Specify Business Problem
Why start with a business problem
companies want to know:
which customers are likely to buy a product
why customers don't purchase the product
why customers do purchase the product
Why customers are dissatisfied
Why customers do not renew their contracts
What leads to extensive use of a product
which internet users to place an ad in front of
which pages on the company website would a specific visitor benefit most from
proposed projects should be evaluated against 3 criteria:
is the project statement presented in the language of business?
does the project statement specify actions that should result from the project?
how could solving this problem impact the bottom line
Chapter 5: Decide on Unit of Analysis
What is a unit of analysis
what, who, where, when
how to determine unit of analysis
Chapter 6: Define Prediction Target
What is a prediction target?
How is it important
without a target there is no way for humans or machines to learn what associations drive an outcome
Chapter 4: Acquire Subject Matter Expertise