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Difference between training and test data distribution

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I think you're confusing the underlying distribution from which both training and test distributions are drawn, with the distributions of the specific train and test draws. Unless the underlying distribution is eg time-sensitive, changed during the time between eg drawing the training and the testing samples, the underlying distribution is identical each time. The goal in learning a machine learning model is typically not to learn the training distribution, but to learn the latent underlying distribution, of which the training distribution is only a sample. Of course, you cannot actually see the underlying distribution, but eg, if you only really cared about learning the training samples, you could simply memorize the training samples in a lookup table, end of story. In reality, you are using the training sample as a proxy into the underlying distribution.



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Enterprises now acknowledge the value of having a highly capable workforce. The ever-changing business landscape demands workers to continually upskill and reskill, giving rise to employee training utilization. Most organizations are now reinforcing their human resources and training and development departments to help them address the need. Large US companies on the average spent $17.7 million on such efforts in 2019. Managing employee training, however, has its own set of challenges.