机器学习中如何选择最优的训练-验证集划分方案？

Nice question—what you're doing here is essentially a form of 5-fold holdout validation (since you're splitting your 100 samples into 5 distinct 20-sample validation sets, each paired with an 80-sample training set). Let's break down how to pick the best split, and even better, how to use all these splits to get a more reliable model:

Step 1: Train all 5 models and collect consistent metrics #

For each model (each tied to a unique train-validation split), train it on its 80-sample training set, then evaluate it on two critical sets:

• Its own 20-sample validation set (record this as val_score)
• Your independent 100-sample test set (record this as test_score)
  Stick to the same metric (accuracy, RMSE, F1-score, etc.) that aligns with your task's goals—consistency here is key to fair comparison.

Step 2: Analyze split quality and generalization ability #

Now look at the scores you’ve gathered to narrow down the best splits:

• Filter out biased splits: If one model’s val_score is drastically higher or lower than the others, that split is likely unrepresentative. For example, maybe its validation set is full of easy edge cases or outliers that don’t match the broader data distribution. Discard these splits immediately—their results won’t reflect real-world performance.
• Prioritize splits with aligned validation and test scores: The best split is the one where val_score is closest to test_score. This means the validation set for that split perfectly mirrors your test set’s distribution—so when you used it to gauge model performance, you got an accurate preview of how the model would perform on unseen data.

Step 3: Don’t limit yourself to a single split (optional but recommended) #

Instead of just picking one "best" split, consider these more robust approaches:

• Use the average score: Take the average test_score across all 5 models as your final performance estimate. This reduces the variance caused by any single biased split.
• Ensemble the models: Combine predictions from all 5 models (e.g., majority vote for classification, weighted average for regression). Since each model was trained on slightly different data, the ensemble often outperforms any single model.
  If you must pick one split for future training, go with the one that has:
  • A val_score near the average of all validation scores (not an outlier)
  • The smallest gap between val_score and test_score
  • A strong test_score (top 2-3 among all models)

Step 4: Double-check data distribution #

Before finalizing, verify each validation set’s distribution matches your overall data:

• For classification tasks: Does each class’s percentage in the validation set match the training data and test set?
• For regression tasks: Do the validation set’s target values have a similar mean/standard deviation to the rest of the data?
  Any split with a skewed distribution should be discarded, even if its scores look good—its validation results won’t be trustworthy.

内容的提问来源于stack exchange，提问作者user67275