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:param with_explanations: Optional, whether individual explanations should be computed for each record. The prediction endpoint must be compatible. If None, will use the value configured in the endpoint.
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:param explanation_method: Optional, method to compute explanations. Valid values are 'SHAPLEY' or 'ICE'. If None, will use the value configured in the endpoint.
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:param n_explanations: Optional, number of explanations to output per prediction. If None, will use the value configured in the endpoint.
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:param n_explanations_mc_steps: Optional, precision parameter for SHAPLEY method, higher means more precise but slower (between 25 and 400).
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:param n_explanations_mc_steps: Optional, precision parameter for SHAPLEY method, higher means more precise but slower (between 25 and 1000).
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If None, will use the value configured in the endpoint.
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:return: a Python dict of the API answer. The answer contains a "result" key (itself a dict)
:param with_explanations: Optional, whether individual explanations should be computed for each record. The prediction endpoint must be compatible. If None, will use the value configured in the endpoint.
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:param explanation_method: Optional, method to compute explanations. Valid values are 'SHAPLEY' or 'ICE'. If None, will use the value configured in the endpoint.
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:param n_explanations: Optional, number of explanations to output per prediction. If None, will use the value configured in the endpoint.
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:param n_explanations_mc_steps: Optional, precision parameter for SHAPLEY method, higher means more precise but slower (between 25 and 400).
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+
:param n_explanations_mc_steps: Optional, precision parameter for SHAPLEY method, higher means more precise but slower (between 25 and 1000).
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If None, will use the value configured in the endpoint.
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:return: a Python dict of the API answer. The answer contains a "results" key (which is an array of result objects)
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