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。

There are also non-tree-based approaches, like Support Vector Machines, which essentially slice up the decision space using arbitrary lines.

还有一些非基于树的方法，例如支持向量机，它本质上是使用任意线来分割决策空间。

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。

This simple approach, of dividing the decision space up into boxes, can be represented by what’s called a decision tree, which would look like this pictorially or could be written in code using If-Statements, like this.

这种将决策空间划分为多个框的简单方法可以用所谓的决策树来表示，它在图形上看起来像这样，或者可以使用 If 语句用代码编写，如下所示。