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

predictLead POST

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Returns the probability of a user being a lead based on their interaction with the service/product and their own attributes (e.g. income, assets, credit score, etc.). Note that the inputs to this method, wherever applicable, should be the column names in the dataset mapped to the column mappings in our system (e.g. column 'user_id' mapped to mapping 'LEAD_ID' 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	A dictionary containing user attributes and/or user's interaction data with the product/service (e.g. number of clicks, items in cart, etc.).
No	explainPredictions	bool	Will explain predictions for leads
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
	LeadScorePrediction

Exceptions:

TYPE	WHEN
DataNotFoundError	`deploymentId` is not found.

Language:

Method

predictClass POST

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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:

Predict API

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Arguments:

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