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o develop a prediction model, data scientists employ a training set historical data in hich the desired outcome is
already kno n). As businesses receive intermediate insights, the modelling process is often very iterative, leading to
refinements in data preparation and model formulation. Data scientists may attempt numerous algorithms ith their
respective parameters for a specific technique to get the best model for the available variables.
S tag e 8 : Evaluation
he data scientist
• revie s the model during development and before deployment to determine its quality and ensure that it correctly
and completely ans ers the business problem.
• interprets the model's quality and efficacy in solving the problem by producing numerous diagnostic metrics and
other outputs such as tables and graphs.
• utilises a testing set for predictive models that is separate from the training set but follo s the same probability
distribution and has a kno n outcome.) he testing set is used to assess the model and ad ust it as necessary.
or a final assessment, the final model is sometimes applied to a validation set as ell.
In addition, data scientists can use statistical significance tests to verify the model's accuracy. his additional evidence
could help ustify model deployment or take action hen the stakes are high, such as ith an e pensive additional
medical procedure or a key aviation ight system.
S tag e 9 : Deployment
he model is deployed into the production environment or an equivalent test environment once it has been built
and authorised by the business sponsors. It is usually used in a restricted capacity until its effectiveness has been
thoroughly assessed. he model might be embedded in a complicated ork o and scoring process run by a
customised application, or it could be as simple as providing a report ith recommendations. Deploying a model into
a live business process frequently necessitates the involvement of additional internal teams, skills, and technology.
S tag e 1 0 : F eedb ack
he organisation receives feedback on the model's effectiveness and impact on the environment in hich it as deployed
by collecting findings from the implemented model. or instance, feedback could come in the form of response rates
to a promotional campaign. Data scientists can utilise this feedback to improve the model's accuracy and utility by
analysing it. hey can automate any or all of the feedback gathering, model assessment, refining, and redeployment
phases to speed up the model refresh process and improve results.
he iterative nature of the problem solving process is sho n by this methodology's o . As data scientists have a
better understanding of the data and models, they typically return to a prior stage to make changes. odels aren't built
once, deployed, and forgotten about instead, they're constantly refined and adapted to changing situations through
feedback, refinement, and redeployment. As a result, both the model and labour that goes into it can continue to add
value to the business for as long as the solution is required.
o ensure a strong result, each stage needs to be continually improved, altered, and t eaked. he three goals of the
frame ork are as follo s
nderstand the question at hand first.
econd, decide on a strategy or method for analysis to address the issue.
ollect, comprehend, prepare, and model the data.
he ultimate ob ective is to get the data scientist to the point here a data model can be created to provide the ans er.
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