Hyper-personalized qualified applicant models

A computerized method comprising:

• obtaining a first plurality of data samples, the data samples indicating a value for a first variable and a value for a second variable;
• identifying a generalized linear mixed effect (GLMix) model to train with a first machine learning algorithm, the GLMix model having a global model and a set of different types of random effects models, wherein each type of random effects model corresponds to a different variable in the first plurality of data samples, and wherein a corresponding instance of each type of random effects model is trained using the value for the first variable or the value for the second variable;
• generating a set of random numbers using a random number generator, the random number generator using output of a hash function as a seed therefor, wherein the hash function takes as input a value for each variable to which a random effects model in the GLMix model corresponds, wherein the hash function is based on a combination of random effects model types;
• assigning a corresponding one of the set of random numbers to the data samples from the first plurality of data samples;
• randomly selecting data samples from the first plurality of data samples, based on the corresponding random number assigned thereto, to assign to a first training set or a first holdout set, wherein the global model is trained using data samples in the first training set;
• training a first iteration of the GLMix model using the first training set;
• obtaining a second plurality of data samples, the data samples in the second plurality of data samples indicating a value for the first variable and a value for the second variable, at least some of the second plurality of data samples being identical to at least some of the first plurality of data samples;
• randomly selecting data samples from the second plurality of data samples to assign to a second training set or a second holdout set using output of the hash function as a seed to the random number generator;
• training a second iteration of the GLMix model using the second training set;
• testing both the first iteration of the GLMix model and the second iteration of the GLMix model using the second holdout set;
• and
• automatically switching from the first iteration of the GLMix model to the second iteration of the GLMix model if the testing indicates superior performance by the second iteration of the GLMix model.

A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising:

• obtaining a first plurality of data samples, the data samples indicating a value for a first variable and a value for a second variable;
• identifying a generalized linear mixed effect (GLMix) model to train with a first machine learning algorithm, the GLMix model having a global model and a set of different types of random effects models, wherein each type of random effects model corresponds to a different variable in the first plurality of data samples, and wherein a corresponding instance of each type of random effects model is trained using the value for the first variable or the value for the second variable;
• generating a set of random numbers using a random number generator, the random number generator using output of a hash function as a seed therefor, wherein the hash function takes as input a value for each variable to which a random effects model in the GLMix model corresponds, wherein the hash function is based on a combination of random effects model types;
• assigning a corresponding one of the set of random numbers to the data samples from the first plurality of data samples;
• randomly selecting data samples from the first plurality of data samples, based on the corresponding random number assigned thereto, to assign to a first training set or a first holdout set, wherein the global model is trained using data samples in the first training set;
• training a first iteration of the GLMix model using the first training set;
• obtaining a second plurality of data samples, the data samples in the second plurality of data samples indicating a value for the first variable and a value for the second variable, at least some of the second plurality of data samples being identical to at least some of the first plurality of data samples;
• randomly selecting data samples from the second plurality of data samples to assign to a second training set or a second holdout set using output of the hash function as a seed to the random number generator;
• training a second iteration of the GLMix model using the second training set;
• testing both the first iteration of the GLMix model and the second iteration of the GLMix model using the second holdout set;
• and
• automatically switching from the first iteration of the GLMix model to the second iteration of the GLMix model if the testing indicates superior performance by the second iteration of the GLMix model.