What are the three main categories of predictive modeling problems? - answer-Descriptive, Predictive, Prescriptive
What are the focus and aim of Descriptive Modeling Problems? - answer-Focus: What happened in the Past
Aim: To describe or interpret observed trends by identifying relationships be...
What are the three main categories of predictive modeling problems? - answer-
Descriptive, Predictive, Prescriptive
What are the focus and aim of Descriptive Modeling Problems? - answer-Focus: What
happened in the Past
Aim: To describe or interpret observed trends by identifying relationships between
variables
What are the focus and aim of Predictive Modeling Problems? - answer-Focus: What
will happen in the future
Aim: To make accurate predictions
What are the focus and aim of Prescriptive Modeling Problems? - answer-Focus: The
impacts of different prescribed decisions
Aim: To answer the what if and what is the best course of action questions
What are characteristics of predictive modeling problems? - answer-issue, questions,
data, impact better solution, update
What is the "issue" characteristic of predictive modeling problems? - answer-There is a
clearly identified and defined business issue to be addressed
What is the "questions" characteristic of predictive modeling problems? - answer-The
issue can be addressed with a few well defined questions
What is the "data" characteristic of predictive modeling problems? - answer-Good and
useful data is available for answering the questions outlined in previous characteristic
What is the "impact" characteristic of predictive modeling problems? - answer-The
predictions will likely drive actions or increase understanding
What is the "better solution" characteristic of predictive modeling problems? - answer-
Predictive analytics likely produces a solution better than the existing approach
What is the "update" characteristic of predictive modeling problems? - answer-We can
continue to monitor and update the models when new data becomes available
What is a General Strategy to produce a meaningful problem definition? - answer-Get to
the root cause of the business issue and make it specific enough to be solvable
What are Specific Strategies to produce a meaningful problem definition? - answer-
Hypothesis and Key Performance Indicators (KPIs)
, What is the "hypothesis" specific strategy to produce a meaningful problem definition? -
answer-Use prior knowledge of the business problem to ask questions and develop
testable hypotheses
What is the "KPIs" specific strategy to produce a meaningful problem definition? -
answer-Select appropriate key performance indicators to provide a quantitative basis for
measuring success
What are some constraints of finding the problem definition? - answer-The availability of
easily accessible and high quality data. Implementation issue, eg the presence of
necessary IT infrastructure and technology to fit complex models efficiently, the cost
and effort required to maintain the selected model
Key features of Data Design - answer-Relevance, sampling, granularity
What is the definition of relevance in reference to data design? - answer-Need to ensure
that the data is unbiased, ie., representative of the environment where the model will
operate.
Population - Important for the data source to be a good proxy of the true population of
interest
Time Frame - Choose a time period which best reflects the business environment of
interest. In general, recent history is better than distant history
What is the definition of Sampling in reference to data design? - answer-The process of
taking a subset of observations from the data source to generate the dataset
Random Sampling - Randomly draw observations from the underlying population
without replacement. Each record is equally likely to be sampled.
Stratified Sampling - Divide the underlying population onto a number of non-overlapping
strata (often w.r.t target) non-randomly, then randomly sample a set number of
observations from each stratum to get a more representative sample.
Systematic sampling - Draw observations according to a set pattern; no random
mechanism controlling which observations are sampled
What is the definition of granularity in reference to data design? - answer-Refers to how
precisely a variable is measured, ie., level of detail for the information contained by the
variable
Key Features of Data Quality Issues - answer-Reasonableness, Consistency, Sufficient
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