1. A system for mitigating digital abuse and/or digital fraud committed using one or more online services of a service provider, the system comprising: an application programming interface that is in operable communication with a remote digital threat mitigation platform and that is configured to generate a request for a global digital threat score; the remote digital threat mitigation platform comprising one or more computing servers that receive, via a network, the request for the global digital threat score and collects digital event data associated with one or more online services of the service provider, wherein the digital threat mitigation platform implementing: a machine learning system that: uses the collected digital event data, as input, into a first machine learning ensemble to generate a global digital threat score, wherein the global digital threat score is agnostic to a category of digital abuse type, and generates the global digital threat score; a machine learning classifier that: generates a digital abuse label that identifies one specific digital abuse type of a plurality of specific digital abuse types based on the collected digital event data, wherein the one specific digital abuse type indicates a category of digital fraud or digital abuse activity that was perpetrated in the collected digital event data by a user; a warping system that: uses input of the generated digital abuse label to warp the first machine learning ensemble to a second machine learning ensemble that generates a specific digital threat score for the one specific digital abuse type, wherein the specific digital threat score indicates a probability or likelihood that the one specific digital abuse type was committed by the user.
4. The system of claim 1 , wherein the warping system comprises a companion machine learning model that infers a candidate likelihood for each of a plurality of digital abuse types, wherein the candidate likelihood indicates a likelihood that each of the one or more of the plurality of digital abuse types exists in the collected digital event data, wherein using the input of the collected digital event data, the companion machine learning model generates the candidate likelihood for each of the plurality of digital abuse types; wherein the digital threat mitigation platform compares the generated candidate likelihood for each of the plurality of digital abuse types to a candidate threshold; wherein when the candidate likelihood for a respective digital abuse type satisfies or exceeds the candidate threshold, generating a notification comprising a subset of the plurality of digital abuse types having candidate likelihoods satisfying the candidate threshold.