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。

Our decision boundaries offer a guess as to what species the moth is.

我们的决策边界提供了关于飞蛾是什么物种的猜测。

These lines that chop up the decision space are called decision boundaries.

这些分割决策空间的线称为决策边界。

Like before, it’s the machine learning algorithm's job to figure out the best lines to provide the most accurate decision boundaries.

和以前一样，机器学习算法的工作是找出最佳路线以提供最准确的决策边界。

If we add a third feature, let’s say, length of antennae, then our 2D lines become 3D planes, creating decision boundaries in three dimensions.

如果我们添加第三个特征， 比如说天线的长度， 那么我们的 2D 线就会变成 3D 平面，从而在三维中创建决策边界。

Now, using these decision boundaries, if we go out into the forest and encounter an unknown moth, we can measure its features and plot it onto our decision space.

现在，使用这些决策边界，如果我们进入森林并遇到一只未知的飞蛾，我们可以测量它的特征并将其绘制到我们的决策空间中。