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1. Using mutable objects as default arguments
Python creates a function’s default values once, when the function is defined—not afresh for each call. If a default list or dictionary is mutated, later calls can inherit that state. The Python programming FAQ documents this behavior.
def add_label(label, labels=None):
if labels is None:
labels = []
labels.append(label)
return labelsUse a sentinel such as None when each call should receive a new mutable object. The function creates that object locally, while callers can still pass an existing list intentionally.
2. Relying on ambient dependencies
A script that works on one machine may fail elsewhere because it silently depends on packages installed outside the project or on a different interpreter. Use an intentional project environment and record the dependencies needed to recreate it. Python’s packaging guidance describes virtual environments as isolated contexts for project packages and notes that shell activation is optional: Python Virtual Environments.
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Do not treat an activated shell as proof that a program is using the intended environment. Activation adjusts shell behavior; code that needs to check its interpreter should inspect the interpreter or environment directly. Keep the environment and dependency information with the experiment so a later run has a known setup.
3. Leaving randomness uncontrolled when comparing runs
Randomized steps can change the data or computation being compared. In scikit-learn, shuffled cross-validation with random_state=None can produce different splits; supplying an integer seed makes those splits repeatable. See the scikit-learn cross-validation guide.
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For deep-learning work, seed the relevant random-number generators and record what you seeded. A seed controls some sources of variation; it is not a promise that every result will match across machines or software setups.
4. Treating reproducibility as a single seed
A seed is only one part of an experiment’s setup. PyTorch warns that “Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms.” Its reproducibility guidance also explains that deterministic choices can help in a specified environment but may reduce performance.
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When reporting or revisiting a result, record the framework version, device, and relevant randomness and determinism settings. A score detached from those details is difficult to interpret or reproduce.
5. Using weak or inconsistent evaluation splits
A model score is a property of both the model and the evaluation procedure. Changing the split or validation method between experiments can make a score change even when the code change you are investigating is not responsible.
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Choose an evaluation protocol appropriate to the task, then keep it consistent when comparing candidates. For shuffled cross-validation, control the random state when you need repeatable folds. The scikit-learn guide explains how cross-validation works and how shuffled folds can vary; it does not establish one split strategy as right for every problem.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Reporting an error without a minimal reproduction
A traceback without the code and data that trigger it often leaves others guessing. If an exception remains unexplained, scikit-learn’s Frequently Asked Questions recommends providing a minimal reproducible example and the full traceback.
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Make the example small enough to run quickly, but complete enough to show the failure:
- Include the relevant imports and a short code sample.
- Use a small toy dataset that still reproduces the problem.
- Include the complete traceback rather than only its final line.
- Note the relevant package versions and execution device when they may affect the error.
7. Changing several things at once
If you change the model, preprocessing, data split, and random-state settings in one run, a changed score cannot tell you which change mattered. This is a practical experiment-design problem: make one relevant change at a time where possible, and record the configuration for each run.
Some experiments require changing more than one factor, but make those changes explicit and compare them under the same evaluation setup. Alongside each metric, capture the environment and package versions, data split and validation procedure, random-state settings, hardware or device, and whether deterministic algorithms were enabled. That context helps distinguish a meaningful model improvement from a changed setup.
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