的重点sample_weights是赋予特定样本权重（例如，通过它们的重要性或确定性）；不是特定的类。
显然，“平衡精度”是（来自用户指南）：

每类召回分数的宏观平均值

因此，由于分数是跨班级平均的 - 只有班级内的权重很重要，而不是班级之间......并且您的权重在班级内是相同的，并且仅在班级之间变化。

明确地（再次来自用户指南）：

$$\hat{w}_i = \frac{w_i}{\sum_j{1(y_j = y_i) w_j}}$$

即第i个样本通过将其权重除以具有相同标签的样本的总权重来重新加权。

现在，如果您愿意，您可以使用简单的准确度分数，并根据需要插入权重。

在以下示例中：

from sklearn.metrics import balanced_accuracy_score, accuracy_score

y_true = [0, 1, 0, 0, 1, 0, 1, 1, 1, 1]
y_pred = [0, 1, 0, 0, 0, 1, 1, 1, 1, 1]

some_sample_weights =[10, 1, 1, 1, 10, 1, 0.5, 0.5, 0.5, 0.5]
weights_by_class =[1 if y==1 else 1000 for y in y_true]

print('with some weights: {:.2f}'.format(balanced_accuracy_score(y_true, y_pred, sample_weight=some_sample_weights)))
print('without weights: {:.2f}'.format(balanced_accuracy_score(y_true, y_pred)))
print('with class weights in balanced accuracy score: {:.2f}'.format(balanced_accuracy_score(y_true, y_pred, sample_weight=weights_by_class)))
print('with class weights in accuracy score: {:.5f}'.format(accuracy_score(y_true, y_pred, sample_weight=weights_by_class)))

class_sizes = [sum((1 for y in y_true if y==x))/len(y_true) for x in (0,1)] 
weights_by_class_manually_balanced = [w/class_sizes[y] for w, y in zip(weights_by_class, y_true)]

print('with class weights in accuracy score (manually balanced): {:.5f}'.format(accuracy_score(y_true, y_pred, sample_weight=weights_by_class_manually_balanced)))

你得到：

with some weights: 0.58
without weights: 0.79
with class weights in balanced accuracy score: 0.79
with class weights in accuracy score: 0.75012
with class weights in accuracy score (manually balanced): 0.75008

如你看到的：

• 在平衡准确度得分中使用类别权重并不重要；他们刚刚调整回班级规模。
• 在准确度分数中使用类权重非常接近 75%（4 个0标签中有 3 个被正确分类），并且根据类大小重新调整权重并不重要（准确度有点低，因为班级0更大）