DETERMINING A HYPERPARAMETER FOR INFLUENCING NON-LOCAL SAMPLES IN MACHINE LEARNING
Methods, computer readable media, and devices for determining a hyperparameter for influencing non-local samples in machine learning are disclosed. One method may include identifying a set of local samples associated with a first entity, identifying a set of non-local samples comprising samples associated with a plurality of entities other than the first entity, assigning a local sample weight to one or more samples of the set of local samples, determining a range of non-local sample weights, determining a range of hyperparameters based on the range of non-local sample weights, determining an optimized hyperparameter based on the range of hyperparameters, assigning an optimized non-local sample weight to one or more samples of the set of non-local samples, and generating a prediction using machine learning.
1 . A computer-implemented for determining a hyperparameter for influencing non-local samples in machine learning, the method comprising:
identifying a set of local samples associated with a first entity;
identifying a set of non-local samples comprising samples associated with a plurality of entities other than the first entity;
assigning a local sample weight to one or more samples of the set of local samples;
determining a range of non-local sample weights;
determining a range of hyperparameters based on the range of non-local sample weights;
determining an optimized hyperparameter based on the range of hyperparameters;
assigning an optimized non-local sample weight to one or more samples of the set of non-local samples, the optimized non-local sample weight based on the optimized hyperparameter; and
generating a prediction using machine learning, the prediction associated with the first entity and being based on:
the set of local samples;
the set of non-local samples;
the local sample weight; and
the optimized non-local sample weight.
2 . The computer-implemented method of claim 1 , wherein the local sample weight is 1.
3 . The computer-implemented method of claim 1 , wherein the range of non-local sample weights is between:
a total number of samples in the set of local samples over a total number of samples in the set of local samples and the set of non-local samples; and
the integer value 1.
4 . The computer-implemented method of claim 1 , wherein determining a range of hyperparameters based on the range of non-local sample weights comprises, for any one non-local sample weight, determining an associated hyperparameter to be a ratio of a total number of samples in the set of local samples to a difference between a total number of samples in the set of non-local samples and the total number of samples in the set of local samples multiplied by the one non-local sample weight plus the total number of samples in the set of local samples.
5 . The computer-implemented method of claim 1 , wherein determining a range of hyperparameters based on the range of non-local sample weights comprises, for any one non-local sample weight, determining an associated hyperparameter to be a ratio of a total number of samples in the set of local samples multiplied by the local sample weight to a difference between a total number of samples in the set of non-local samples and the total number of samples in the set of local samples multiplied by the one non-local sample weight plus the total number of samples in the set of local samples multiplied by the local sample weight.
6 . The computer-implemented method of claim 1 , wherein determining a range of hyperparameters based on the range of non-local sample weights comprises, for any one non-local sample weight, determining an associated hyperparameter to be a ratio of a sum of local sample weights assigned to the one or more samples of the set of local samples to a sum of non-local sample weights assigned to the one or more samples of the set of non-local samples plus the sum of local sample weights assigned to the one or more samples of the set of local samples.
7 . The computer-implemented method of claim 1 , wherein determining an optimized hyperparameter based on the range of hyperparameters comprises performing a grid search.
8 . The computer-implemented method of claim 1 , wherein determining an optimized hyperparameter based on the range of hyperparameters comprises utilizing a Bayesian optimization.
9 . The computer-implemented method of claim 1 , wherein the prediction is a prediction of an action to be taken by one or more individuals associated with the first entity.
10 . A non-transitory machine-readable storage medium that provides instructions that, if executed by a processor, are configurable to cause the processor to perform operations comprising:
identifying a set of local samples associated with a first entity;
identifying a set of non-local samples comprising samples associated with a plurality of entities other than the first entity;
assigning a local sample weight to one or more samples of the set of local samples;
determining a range of non-local sample weights;
determining a range of hyperparameters based on the range of non-local sample weights;
determining an optimized hyperparameter based on the range of hyperparameters;
assigning an optimized non-local sample weight to one or more samples of the set of non-local samples, the optimized non-local sample weight based on the optimized hyperparameter; and
generating a prediction using machine learning, the prediction associated with the first entity and being based on:
the set of local samples;
the set of non-local samples;
the local sample weight; and
the optimized non-local sample weight.
11 . The non-transitory machine-readable storage medium of claim 10 , wherein the range of non-local sample weights is between:
a total number of samples in the set of local samples over a total number of samples in the set of local samples and the set of non-local samples; and
the integer value 1.
12 . The non-transitory machine-readable storage medium of claim 10 , wherein determining a range of hyperparameters based on the range of non-local sample weights comprises, for any one non-local sample weight, determining an associated hyperparameter to be a ratio of a total number of samples in the set of local samples to a difference between a total number of samples in the set of non-local samples and the total number of samples in the set of local samples multiplied by the one non-local sample weight plus the total number of samples in the set of local samples.
13 . The non-transitory machine-readable storage medium of claim 10 , wherein determining an optimized hyperparameter based on the range of hyperparameters comprises performing a grid search.
14 . The non-transitory machine-readable storage medium of claim 10 , wherein determining an optimized hyperparameter based on the range of hyperparameters comprises utilizing a Bayesian optimization.
15 . The non-transitory machine-readable storage medium of claim 10 , wherein the prediction is a prediction of an action to be taken by one or more individuals associated with the first entity.
16 . An apparatus comprising:
a processor; and
a non-transitory machine-readable storage medium that provides instructions that, if executed by a processor, are configurable to cause the processor to perform operations comprising:
identifying a set of local samples associated with a first entity;
identifying a set of non-local samples comprising samples associated with a plurality of entities other than the first entity;
assigning a local sample weight to one or more samples of the set of local samples;
determining a range of non-local sample weights;
determining a range of hyperparameters based on the range of non-local sample weights;
determining an optimized hyperparameter based on the range of hyperparameters;
assigning an optimized non-local sample weight to one or more samples of the set of non-local samples, the optimized non-local sample weight based on the optimized hyperparameter; and
generating a prediction using machine learning, the prediction associated with the first entity and being based on:
the set of local samples;
the set of non-local samples;
the local sample weight; and
the optimized non-local sample weight.
17 . The apparatus of claim 16 , wherein the range of non-local sample weights is between:
a total number of samples in the set of local samples over a total number of samples in the set of local samples and the set of non-local samples; and
the integer value 1.
18 . The apparatus of claim 16 , wherein determining a range of hyperparameters based on the range of non-local sample weights comprises, for any one non-local sample weight, determining an associated hyperparameter to be a ratio of a total number of samples in the set of local samples to a difference between a total number of samples in the set of non-local samples and the total number of samples in the set of local samples multiplied by the one non-local sample weight plus the total number of samples in the set of local samples.
19 . The apparatus of claim 16 , wherein determining an optimized hyperparameter based on the range of hyperparameters comprises performing a grid search.
20 . The apparatus of claim 16 , wherein determining an optimized hyperparameter based on the range of hyperparameters comprises utilizing a Bayesian optimization.