Set Seed in XLMiner: Use Integer 12345, 42, 2024 for Consistent Partitions
If you’ve ever rerun a regression in Excel and found the training‑test split shuffled in a way that altered your results, you know how frustrating that can be. The culprit isn’t the data itself; it’s the random seed that XLMiner applies behind the scenes. Without a fixed seed, each execution redraws the partitions, making it hard to compare model tweaks or reproduce a published benchmark.
That’s why a simple checkbox can save you hours of back‑and‑forth. By anchoring the randomness to a memorable integer—say 12345, 42, or 2024—you lock the split in place. The next time you hit “Run,” the same rows will land in the same buckets, and you’ll have a clear audit trail if you note the seed in the Model Log.
This practice isn’t a fancy trick; it’s basic reproducibility, the kind of rigor that separates a one‑off experiment from a reliable analysis pipeline.
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When using XLMiner's partition functionality (found in most model dialogs): - Check the box labeled "Set seed" (it's unchecked by default) - Enter a specific integer: 12345, 42, 2024, or any memorable number - Document this seed value in the Model Log Now, every time the model is run with this seed:
When using XLMiner's partition functionality (found in most model dialogs): - Check the box labeled "Set seed" (it's unchecked by default) - Enter a specific integer: 12345, 42, 2024, or any memorable number - Document this seed value in the Model Log Now, every time the model is run with this seed: - Identical training/validation/test splits - Identical model performance metrics - Identical predictions for the same observations - Perfect reproducibility Here is an example from the loan approval dataset without seed (three runs of identical logistic regression): - Run 1: Validation Accuracy = 92.4%, F1 = 0.917 - Run 2: Validation Accuracy = 91.8%, F1 = 0.923 - Run 3: Validation Accuracy = 92.1%, F1 = 0.919 And with with seed=12345 (three runs of identical logistic regression): - Run 1: Validation Accuracy = 92.1%, F1 = 0.928 - Run 2: Validation Accuracy = 92.1%, F1 = 0.928 - Run 3: Validation Accuracy = 92.1%, F1 = 0.928 The difference matters enormously for credibility.
Did the simple act of ticking “Set seed” close the reproducibility gap? For many Excel‑centric teams, the answer feels immediate: a single integer—12345, 42, 2024—locks the partitioning process, turning a stochastic step into a repeatable one. Yet the article reminds readers that this is just one of five hard‑earned lessons, suggesting that consistency in XLMiner still depends on disciplined logging and documentation.
Because the seed is stored in the Model Log, anyone revisiting the workbook can rerun the same model and expect identical splits, provided no other settings change. Still, the piece stops short of confirming whether this practice alone eliminates all variability; the broader workflow may still introduce hidden differences. Moreover, the guidance assumes users will remember or record the chosen number, an assumption that may not hold in larger groups.
In short, setting a seed offers a concrete step toward reproducible Excel AI work, but its effectiveness hinges on consistent application and broader process controls that the article does not fully explore.
Further Reading
- Papers with Code Benchmarks - Papers with Code
- Chatbot Arena Leaderboard - LMSYS
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Common Questions Answered
How does checking the "Set seed" box in XLMiner affect training/validation/test splits?
When the "Set seed" box is checked and a specific integer (e.g., 12345, 42, or 2024) is entered, XLMiner uses that seed to generate the same random partitions each time. This ensures that the training, validation, and test splits remain identical across runs, providing consistent model performance metrics.
Why is it important to document the seed value in the Model Log after setting a seed in XLMiner?
Documenting the seed value in the Model Log creates a permanent record of the exact integer used to control randomness. This practice enables anyone revisiting the workbook to reproduce the exact partitioning and results, supporting reproducibility and auditability of the analysis.
Can any memorable integer be used as a seed in XLMiner, and are there recommended examples?
Yes, any integer can serve as a seed in XLMiner, but the article suggests using memorable numbers such as 12345, 42, or 2024 for ease of recall. Choosing a memorable seed helps analysts quickly reference and reapply the same randomness control without consulting external notes.
What reproducibility benefits does setting a seed provide when rerunning regressions in Excel with XLMiner?
Setting a seed locks the stochastic partitioning step, so each rerun yields identical predictions for the same observations and the same performance metrics. This eliminates variability caused by random splits, allowing analysts to compare model tweaks reliably and close the reproducibility gap.