Chapter 2-7: (Chapter 2: Automating Machine Learning (Criteria for AutoML…
Chapter 2: Automating Machine Learning
process of automating machine learning, very time/knowledge intensive process
exploratory data analysis
algorithm selection and hyperparameter tuning
autoML is not automatic ML
still need people to analyze
Criteria for AutoML Excellence
ease of use
understanding and learning
generalizable across contexts
Chapter 3. Specify Business Problem
perfect accuracy is of no value if wrong problem is addressed
must be described and shared with stakeholders
needs to be audience focused
Chapter 7: Identify Success Criteria
consider these factors
who will use the model?
is management on board with project?
can the model drivers be visualized?
how much value can the model produce?
too predictive or not predictive enough
missions and factors leading to success
external environment that provided data for your analysis changes drastically
Chapter 4: Acquire Subject Matter Expertise
need some sort of expertise to be capable
Chapter 5: Decide on Unit of Analysis
involves intricate understanding of a problem's real world context
what prediction target is usually will tell unit of analysis
everything will be different
Chapter 6: Define Prediction Target
behavior of the "thing" we need to know about the future
without a target, there is no way for humans or machines to learn what associations drive an outcome