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chapters 2-7 (Ch 2 Auto ML (define objectives, aquire and explore data,…
chapters 2-7
Ch 2 Auto ML
define objectives
aquire and explore data
model data
interperet and communicate
implement and document and maintain
expolratory data analysis
feature engineering
algorithym selection
model diagnostics
Accuracy
productivity
Ease of use
Understanding and learning
resource availability
process transparency
generalize across contexts
recommend actions
ch 5 decide on unit of analysis
who
what
where
where
what is prediction target
each application will be different
cp 7 success risk and continuation
who will use the model
is managment on board
can model drivers be visualized
how much value can it produce
success criteria
risk
ethical
moedl
data
privacy
cultural
enviromental
ch 3 specify business problem
opportunity
knowledge
useful information
benefit
ch 4 acquire subject matter expertise
without, how can findings be useful
understand real world context
ch 6 prediction target
behavious of a 'thing' we want to know for the future
good
bad