Because software packages use random draws of the observations to partition data, the results will not be identical to a fixed partitioning of the observations.

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Software packages often use random sampling or partitioning methods to divide data, especially in machine learning tasks like cross-validation, leading to results that can vary from run to run unless a fixed seed for randomness is used, making them different from results obtained through a fixed, deterministic partitioning of the data.

Because Software Packages Use Random Draws of the Observations to Partition