Hey there! Let's break down your questions clearly, starting with the basics of what pos_weight does, then diving into how it impacts recall and precision.

1. What's the effect of the pos_weight parameter in BCEWithLogitsLoss? #

First, remember that BCEWithLogitsLoss combines a Sigmoid layer and binary cross-entropy (BCE) loss into one single class. By default, it treats positive and negative samples equally when calculating loss.

The pos_weight parameter lets you assign a custom weight to positive samples in the loss calculation. Mathematically, the modified loss formula becomes:

loss = -pos_weight * y_true * log(y_pred) - (1 - y_true) * log(1 - y_pred)

(Where y_true is the ground truth label, y_pred is the model's predicted probability after Sigmoid.)

• When pos_weight = 1, this reduces to the standard BCE loss—no bias toward either class.
• When pos_weight > 1, positive samples contribute more to the total loss. This tells the model: "Mistakes on positive samples matter more than mistakes on negative ones."
• When pos_weight < 1, positive samples contribute less to the loss, so the model prioritizes avoiding mistakes on negative samples instead.

This is especially useful for imbalanced datasets (e.g., 90% negative samples, 10% positive)—you can use pos_weight >1 to make the model pay more attention to the rare positive class.

2. How to interpret "pos_weight >1 boosts recall, <1 boosts precision"? #

First, let's quickly recap the core definitions to set the stage:

• Recall: The percentage of actual positive samples that the model correctly identifies (TP / (TP + FN)). It measures how well we "catch" all positive cases.
• Precision: The percentage of predicted positive samples that are actually positive (TP / (TP + FP)). It measures how "accurate" our positive predictions are.

When pos_weight >1: #

By amplifying the loss for positive samples, the model becomes far more worried about missing positive cases (false negatives, FN). To minimize loss, it will err on the side of predicting "positive" more often—even for samples it's not 100% sure about.

• This reduces FN, which directly increases recall (since recall = TP/(TP+FN)).
• However, this can lead to more false positives (FP)—the model might label some negative samples as positive. As a result, precision tends to drop (since precision = TP/(TP+FP)).

Think of this like a medical screening: you want to catch every possible case (high recall), so you set pos_weight high. Even if that means some healthy people get flagged for further testing (FP), you don't want to miss any sick patients (FN).

When pos_weight <1: #

Here, we're reducing the weight of positive samples in the loss. Now the model cares more about avoiding false positives (FP)—because misclassifying a negative sample as positive hits the loss harder than misclassifying a positive as negative.

• The model will only predict "positive" when it's very confident, which cuts down on FP. This increases precision (since we're making fewer wrong positive calls).
• But this can lead to more false negatives (FN)—the model might hesitate to label borderline positive samples as positive, so some actual positives get missed. This causes recall to drop.

An example here is spam filtering: you don't want to mark a legitimate email as spam (FP), so you set pos_weight low. The model will only flag obvious spam, keeping precision high, but might let some tricky spam slip through (lower recall).

内容的提问来源于stack exchange，提问作者Yiwei Jiang