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Documentation for profiles.yaml #19

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Documentation for profiles.yaml #19

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Changes to be committed:
modified: models/profiles.yaml

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	modified:   models/profiles.yaml
@joker2411 joker2411 requested a review from dpatchigolla October 5, 2023 12:48
label_value: 1 # Value of label column for which prediction is generated
prediction_horizon_days: 7 # Number of days in future for which we want to predict
features_profiles_model: 'shopify_user_features' # Model name
output_profiles_ml_model: *model_name # Name of output model based on current model to dinstinguish between multiple models
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  1. Can you add eligible_users and keep that blank string, with comment explaining what it is?
  2. Also add preprocessing: ignore_features: [], again with a comment.
  3. Does the preprocessing get captured in predict if we add here? Is it needed?

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ignore_features, timestamp_columns are being used in predict script as well. I, therefore, have passed the same configs to predict as well. Though in future, we can keep both of them to some other key, because these 2 are being used in both train and predict out of all the keys in preprocessing.

Changes to be committed:
	modified:   models/profiles.yaml
@joker2411 joker2411 requested a review from dpatchigolla October 6, 2023 04:31
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