This Juejin article covers a foundational machine learning technique: Recursive Feature Elimination (RFE). The author uses a "company performance cut" analogy — train on all features first, let the model score each one's performance, drop the worst one, retrain, repeat, then select the feature subset with the highest cross-validation score from any round.
Running through a heart disease prediction case, out of 30 features, only 8–10 survive — and the model actually scores higher than when using all features. What the article doesn't mention: this "less is more" reasoning has shown up repeatedly in LLM (large language model, i.e. AI that can understand and generate text) training over the past two years. OpenAI, Anthropic, and Alibaba all use "quality filtering" to prune training data — the logic is essentially the same as this RFE piece.
What This Is
Recursive Feature Elimination (RFE) follows a four-step loop: train a base model (e.g. random forest or logistic regression) on all features → have the model score each feature's importance → drop the least important ones → retrain, score, drop, until only one feature remains. Each round runs cross-validation, and the final selection isn't "whatever's left" — it's the feature combination from the historical round that scored highest, i.e. the "inflection point."
At the code level, the author wraps a class with three core methods: _evaluate_subset uses cross-validation for scoring (avoiding the "overfitting" trap where a model only performs well on training data and collapses on new data), _extract_importance pulls feature importance based on model type (tree models use feature_importances_, linear models use the absolute value of coef_), and _locate_optimum picks the highest score from the historical log. Three output interfaces come bundled: select(), summary_table(), and plot_curve(). The plotted curve typically rises then falls — the peak is the optimal feature count.
Industry View
The technical depth here isn't high — sklearn (Python's most mainstream machine learning library) ships with its own RFE implementation, and the author mainly rebuilds the wheel while writing it in an accessible way. Precisely because of that, two camps coexist in the comments.
Supporters argue: this example is perfect for explaining the value of "Feature Engineering" (manually selecting and processing input data) to non-algorithm roles. Colleagues doing risk control, user segmentation, or sales forecasting can directly transplant the thinking. Critics counter: RFE has long been replaced in industry by automated feature selection (e.g. recursive methods based on SHAP values — a tool that explains each feature's contribution to predictions), and the author's use of a cleanly structured heart disease dataset is unrealistic — on real business data with thousands of columns and missing values everywhere, this method basically can't run.
One more controversy: the article ends with a cloud drive link for downloading the code. That's unusual outside Juejin and led some to wonder whether it's a soft-ad traffic play.
Impact on Regular People
For enterprise IT: If your company is rolling out a machine learning project, remember that "more data means a better model" is an illusion. Spending two weeks on feature selection early on often saves more trouble than swapping in a pricier model later.
For individual careers: The judgment framework of "cut what's unimportant, keep what's critical" applies to any prioritization scenario — not every customer request, job skill, or meeting agenda deserves equal investment.
For the consumer market: No direct impact is visible yet. This article targets developers and produces no perceptible change for ordinary consumers.