1. A method to improve predictive capability of a machine learning system, the method comprising: receiving, by a computer, training data that includes one or more points; identifying, by the computer, a training distribution of the one or more points of the training data; receiving, by the computer, test data that includes one or more points; identifying, by the computer, information about a test distribution of the one or more points of the test data; identifying, by the computer, one or more coordinates for the one or more points of the training data and the one or more points of the test data; determining, for each identified coordinate and by the computer differences between the one or more points of the test data and the one or more points of the training data; determining, by the computer, weights for the one or more points of the training data based on the determined differences, wherein the weights are adapted to cause the training distribution to conform to the test distribution in response to the weights being applied to the training distribution; generating, by the computer, a weighted function based on the determined weights and the training data; and generating, by the computer, a first output based on an application of an input to the generated weighted function, wherein the first output is different than a second output generated by an application of the input to a non-weighted function, wherein the first output and the second output respectively correspond to a first predictive capability and a second predictive capability of the machine learning system, and wherein the first predictive capability is greater than the second predictive capability.