sklearn中RandomForestClassifier的class_weight=None与"balanced_subsample"是否等价？

Short answer: Absolutely not—these two settings behave very differently, especially on imbalanced datasets. Let’s break down the details to clear up any confusion with the documentation.

What class_weight=None Does #

When you set class_weight=None, the model assigns an equal weight of 1 to every training sample, regardless of its class or the overall class distribution in your dataset. There’s no adjustment for class imbalance—each sample contributes equally to the loss function during tree training.

What class_weight="balanced_subsample" Does #

This setting dynamically adjusts class weights for each individual bootstrap sample used to train a tree in the forest. The weight for each class is calculated using this formula:

n_samples_in_bootstrap / (n_classes * count_of_class_in_bootstrap)

This means if a bootstrap sample ends up with a skewed class distribution (e.g., 90% class A, 10% class B), the model will weight the minority class B more heavily to counteract the imbalance in that specific subsample.

Key Differences to Note #

• On balanced datasets: You might not see a huge performance gap, but the underlying logic is still distinct. None uses uniform weights for all samples, while "balanced_subsample" still recalculates weights per bootstrap sample (even if they’re roughly equal).
• On imbalanced datasets: The difference becomes stark. None lets the majority class dominate training, while "balanced_subsample" actively adjusts weights in each tree to give minority classes more influence.
• Documentation clarification: You might have confused this with the note that when bootstrap=False, "balanced_subsample" behaves the same as "balanced" (since there’s no bootstrap sampling, it uses the full training set’s class distribution). But neither of these is equivalent to None.

Quick Example to Illustrate #

Suppose you have a dataset with 1000 samples: 900 class 0, 100 class 1.

• With class_weight=None: Every sample (whether class 0 or 1) has a weight of 1.
• With class_weight="balanced_subsample": For a bootstrap sample that picks 850 class 0 and 150 class 1, class 0 gets a weight of 1000/(2*850) ≈ 0.588, class 1 gets 1000/(2*150) ≈ 3.333. This makes the model prioritize learning from the minority class in that tree.

内容的提问来源于stack exchange，提问作者Free Palestine