If we raise it, we misclassify more Luna moths.

如果我们提高它，我们就会对更多的 Luna 飞蛾进行错误分类。

In this case, we’d predict it’s a Luna Moth.

在这种情况下，我们会预测它是月神蛾。

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。

In this example, the output neuron with the highest value is our decision: Luna Moth.

在这个例子中，具有最高值的输出神经元是我们的决定：Luna Moth。

On the other hand, 82 luna moths would be correct, with 18 falling onto the wrong side.

另一方面，82 只月亮飞蛾是正确的，18 只落在错误的一边。

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。

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。

[Sara] This is the perfect time really to be looking for a moths because a lot of them are mating right now especially those macro moths that everybody loves to see like the luna moth.

尤其是那些大家都喜欢看的大型飞蛾， 比如 luna 飞蛾。