5. A method for mitigating digital abuse and/or digital fraud occurring using online services, the method comprising: receiving, via an application program interface, a request for a global digital threat score, the global digital threat score indicating a likelihood of digital fraud and/or digital abuse; collecting digital event data, via a network, from at least one remote source of digital event data, wherein the digital event data comprises data relating to one or more activities and/or events performed using one or more online services of a service provider; using the collected digital event data as input into a machine learning system of a digital threat mitigation platform, the machine learning system comprising one or more computing servers implementing a primary machine learning ensemble that predicts the likelihood of digital fraud and/or digital abuse from the collected digital event data; generating by the machine learning system the global digital threat score based on the input of the collected digital event data, wherein the global digital threat score is agnostic to a category of digital abuse type; identifying a sub-request for a specific digital threat score for a digital abuse type, wherein the digital abuse type relates to one of a plurality of digital abuse types defined by digital fraud and/or digital abuse activities that is committed by an online user of the online services of the service provider; in response to identifying the sub-request, generating by a machine learning classifier 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; using the generated digital abuse label to warp the primary machine learning ensemble to a secondary machine learning ensemble that generates a specific digital threat score for the one specific digital abuse type based on the input of the collected digital event data, wherein the specific digital threat score indicates a probability or likelihood that the one specific digital abuse type was committed by the user; transmitting, via a score application program interface, the specific digital threat score for the identified digital abuse type in response to the sub-request.
5. A method for mitigating digital abuse and/or digital fraud occurring using online services, the method comprising: receiving, via an application program interface, a request for a global digital threat score, the global digital threat score indicating a likelihood of digital fraud and/or digital abuse; collecting digital event data, via a network, from at least one remote source of digital event data, wherein the digital event data comprises data relating to one or more activities and/or events performed using one or more online services of a service provider; using the collected digital event data as input into a machine learning system of a digital threat mitigation platform, the machine learning system comprising one or more computing servers implementing a primary machine learning ensemble that predicts the likelihood of digital fraud and/or digital abuse from the collected digital event data; generating by the machine learning system the global digital threat score based on the input of the collected digital event data, wherein the global digital threat score is agnostic to a category of digital abuse type; identifying a sub-request for a specific digital threat score for a digital abuse type, wherein the digital abuse type relates to one of a plurality of digital abuse types defined by digital fraud and/or digital abuse activities that is committed by an online user of the online services of the service provider; in response to identifying the sub-request, generating by a machine learning classifier 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; using the generated digital abuse label to warp the primary machine learning ensemble to a secondary machine learning ensemble that generates a specific digital threat score for the one specific digital abuse type based on the input of the collected digital event data, wherein the specific digital threat score indicates a probability or likelihood that the one specific digital abuse type was committed by the user; transmitting, via a score application program interface, the specific digital threat score for the identified digital abuse type in response to the sub-request. 6. The method of claim 5 , further comprising: in response to generating both the global digital threat score and the specific digital threat score for the digital abuse type, implementing one or more digital threat mitigation protocols via the one or more online services that prevent or eliminate the specific digital abuse type when the global digital threat score or the specific digital threat score satisfy or exceed a digital threat threshold.