Scikit-learn中RandomForestRegressor的max_features是否会改变特征重要性？

Great question—this is a common point of confusion when using random forests to identify key feature drivers, especially when moving between bagged trees (default max_features=n_features) and true random forest setups.

The short answer: Yes, feature importance scores will absolutely shift based on your choice of max_features. Here's why, and what that means for your 12-feature project:

Why max_features Impacts Feature Importance #

Scikit-learn calculates random forest feature importance by averaging the reduction in mean squared error (MSE, for regression) that each feature contributes across all trees. Each tree splits nodes by selecting the best feature from a random subset of size max_features.

• When max_features is set to all 12 features (the default for regression), you’re effectively using a bagged ensemble of decision trees—every tree can access every feature when splitting. This means highly predictive features will almost always be chosen for splits, leading to consistently high importance scores.
• When you lower max_features (e.g., to sqrt(12) ≈ 3 or log2(12) ≈ 3), each tree only gets a random slice of features to choose from. A top-tier feature might not be included in some trees’ feature subsets, so those trees have to use lower-impact features instead. This can inflate the importance scores of secondary features and reduce the relative score of your strongest features, simply because they weren’t always available to split on.

What This Means for Your Goal (Identifying Core Drivers) #

Since you’re trying to pin down the true core drivers of your target variable, don’t rely on a single max_features setting. Instead:

• Test multiple max_features values: Try the default (all features), sqrt(n_features), log2(n_features), and even a few custom values (like 4 or 5 for your 12 features).
• Look for stability: Features that maintain high importance across different max_features settings are your most reliable core drivers—their predictive power doesn’t depend on being paired with specific other features.
• Complement with permutation importance: Unlike tree-based importance, permutation importance measures how much the model’s performance drops when a feature’s values are shuffled. It’s agnostic to max_features and gives a more robust measure of a feature’s real-world impact on predictions.

Example Scenario #

Suppose you have a standout feature A that drives 60% of your target’s variance, plus features B, C, D that each drive ~10%.

• With max_features=12, A will have a dominant importance score, and B/C/D will be far behind.
• With max_features=2, some trees won’t get A in their subset, so they’ll split on B or C instead. This will make B/C’s importance scores look higher than they actually are, while A’s score will drop a bit.

By testing both settings, you’ll see that A is consistently top-ranked, while B/C’s scores fluctuate—cluing you in that A is the true core driver.

内容的提问来源于stack exchange，提问作者ThReSholD