Predict API

Once your model is trained, you must deploy the model on Abacus.AI platform to generate predictions. You can use the prediction dashboard to generate the predictions from the trained model. In this section the underlying prediction API and all other additional prediction API methods are discussed for the use case in consideration:

Method
predictChurn POST
Copy POST

Returns the probability of a user to churn out in response to their interactions with the item/product/service. Note that the inputs to this method, wherever applicable, will be the column names in your dataset mapped to the column mappings in our system (e.g. column 'churn_result' mapped to mapping 'CHURNED_YN' in our system).

Arguments:

REQUIRED KEY TYPE DESCRIPTION
Yes deploymentToken str The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
Yes deploymentId str The unique identifier to a deployment created under the project.
Yes queryData dict This will be a dictionary where the 'key' will be the column name (e.g. a column with name 'user_id' in your dataset) mapped to the column mapping USER_ID that uniquely identifies the entity against which a prediction is performed and the 'value' will be the unique value of the same entity.
No explainPredictions bool Will explain predictions for churn
No explainerType str Type of explainer to use for explanations
Note: The arguments for the API methods follow camelCase but for Python SDK underscore_case is followed.

Response:

KEY TYPE DESCRIPTION
success Boolean true if the call succeeded, false if there was an error
ChurnPrediction

Exceptions:

TYPE WHEN
DataNotFoundError

deploymentId is not found.
Language:

Method
predictClass POST
Copy POST

Returns a classification prediction

Arguments:

REQUIRED KEY TYPE DESCRIPTION
Yes deploymentToken str The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model within an application or website.
Yes deploymentId str The unique identifier for a deployment created under the project.
Yes queryData dict A dictionary where the 'Key' is the column name (e.g. a column with the name 'user_id' in your dataset) mapped to the column mapping USER_ID that uniquely identifies the entity against which a prediction is performed and the 'Value' is the unique value of the same entity.
No threshold float A float value that is applied on the popular class label.
No thresholdClass str The label upon which the threshold is added (binary labels only).
No thresholds dict Maps labels to thresholds (multi-label classification only). Defaults to F1 optimal threshold if computed for the given class, else uses 0.5.
No explainPredictions bool If True, returns the SHAP explanations for all input features.
No fixedFeatures list A set of input features to treat as constant for explanations - only honored when the explainer type is KERNEL_EXPLAINER
No nested str If specified generates prediction delta for each index of the specified nested feature.
No explainerType str The type of explainer to use.
Note: The arguments for the API methods follow camelCase but for Python SDK underscore_case is followed.

Response:

KEY TYPE DESCRIPTION
success Boolean true if the call succeeded, false if there was an error
Prediction

Exceptions:

TYPE WHEN
DataNotFoundError

deploymentId is not found.
Language:

© Copyright