仅关注特征重要性时，是否仍需划分训练集与测试集？

Great question—let’s cut to the chase: yes, you should still split your data into training and test sets (or use cross-validation) even if you have no plans to build a predictive model afterward. Here’s why this matters for reliable feature importance analysis:

• Avoid data leakage (the biggest risk)
  If you train your model on the entire dataset to compute feature importance, the model gets exposed to every single data point—including patterns that are just noise or specific to your full dataset. This leads to inflated, unrealistic importance scores that don’t reflect how useful a feature would be in any new, unseen data. For example, a tree-based model might latch onto a random correlation in your full dataset and overstate that feature’s importance, when it’s actually irrelevant in the real world.

• Test the stability of your importance scores
  Feature importance can vary depending on which subset of data you use to train the model. By splitting into train/test sets (or better yet, using k-fold cross-validation), you can check how consistent each feature’s importance is across different training samples. Features with stable, high importance are far more trustworthy than those whose scores jump around—those volatile ones are likely tied to noise, not true predictive signal.

• Get a realistic measure of feature value
  Even if you’re not predicting anything, feature importance is supposed to tell you how much a feature contributes to explaining the target variable in a generalizable way. If you skip splitting, you’re only measuring how well the feature works on your specific dataset, not its actual value as a meaningful predictor. This makes your analysis less credible if you want to draw conclusions that hold up beyond your current data.

If your dataset is extremely small and splitting would leave you with too little data to train a meaningful model, opt for k-fold cross-validation instead of a single train/test split. This lets you use all your data while still avoiding leakage and testing stability.

内容的提问来源于stack exchange，提问作者26slices