Get a completed set of predictions.

Retrieve predictions that have previously been computed.
Training predictions encoded either as JSON or CSV.
If CSV output was requested, the returned CSV data will contain the following columns:

  • For regression projects: row_id and prediction.
  • For binary classification projects: row_id, prediction,
    class_<positive_class_label> and class_<negative_class_label>.
  • For multi classification projects: row_id, prediction and a
    class_<class_label> for each class.
  • For time-series, these additional columns will be added: forecast_point,
    forecast_distance, timestamp, and series_id.

.. minversion:: v2.21

* If `explanationAlgorithm` = 'shap', these additional columns will be added:
  triplets of (`Explanation_<i>_feature_name`,
  `Explanation_<i>_feature_value`, and `Explanation_<i>_strength`) for `i` ranging
  from 1 to `maxExplanations`, `shap_remaining_total` and `shap_base_value`. Binary
  classification projects will also have `explained_class`, the class for which
  positive SHAP values imply an increased probability.
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