If we lower our wingspan decision boundary, we misclassify more Emperor moths as Lunas.

如果我们降低我们的翼展决策边界，我们会将更多帝蛾错误分类为 Lunas。

Let’s start with a simple example: deciding if a moth is a Luna Moth or an Emperor Moth.

让我们从一个简单的例子开始：判断一只飞蛾是月神蛾还是帝王蛾。

To get that, we’d send an entomologist out into a forest to collect data for both luna and emperor moths.

为此，我们会派一名昆虫学家到森林里去收集月神蛾和帝王蛾的数据。

At the other end, we have an output layer, with two neurons: one for Emperor Moth and another for Luna Moth.

在另一端，我们有一个输出层，有两个神经元：一个用于 Emperor Moth，另一个用于 Luna Moth。

We can add another division that says additionally mass must be less than .75 in order for our guess to be Emperor Moth.

我们可以添加另一个除法，额外说明质量必须小于 0.75 才能让我们猜测是帝蛾。

I’m just going to eyeball it and say anything less than 45 millimeters in wingspan is likely to be an Emperor Moth.

我只是盯着它看，然后说任何翼展小于 45 毫米的都可能是帝王蛾。

If we look closely at our data, we can see that 86 emperor moths would correctly end up inside the emperor decision region, but 14 would end up incorrectly in luna moth territory.

如果我们仔细查看我们的数据，我们可以看到 86 只帝王蛾会正确地落入帝王决策区域内，但有 14 只帝王蛾会错误地落在月神蛾领地内。