You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?
A.
Use your model to run predictions on fresh customer input data.
B.
Monitor your model performance, and make any adjustments needed.
C.
Delineate what data will be used for testing and what will be used for training the model.
D.
Test and evaluate your model on your curated data to determine how well the model performs.
Option C - you've just concluded processing data, ending up with clean and prepared data for the model. Now you need to decide how to split the data for testing and for training. Only afterwards, you can train the model, evaluate it, fine tune it and, eventually, predict with it
Anwser D is correct:
the next step in the machine learning lifecycle is to evaluate the model
Delineate test/train data should have been done BEFORE or during data processing
The machine learning life cycle typically involves planning, data preparation, model engineering, model evaluation, model deployment, and monitoring/maintenance.
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