And in this module we have a class called decision tree classifier.

在这个模块中，我们有一个名为决策树分类器的类。

In this lecture we're going to use a very simple algorithm called decision tree.

在本讲中，我们将使用一种非常简单的算法，称为决策树。

This class implements the decision tree operator, okay?

这个类实现了决策树运算符，好吗？

Decision Trees are just one basic example of a machine learning technique.

决策树只是机器学习技术的一个基本示例。

A few algorithms even use many decision trees working together to make a prediction.

一些算法甚至使用许多决策树一起工作来做出预测。

So, at the end, let's create an object called models nd set it to anew instance of decision, tree classifier. Like this.

所以，最后，让我们创建一个名为模型的对象， 并将其设置为一个新的决策实例，即树分类器。 像这样。

So they are not always the same, also the more columns or the more features we have, our decision tree is going to get more complex.

所以它们并不总是相同的，而且我们拥有的列越多或特征越多，我们的决策树就会变得越复杂。

A machine learning algorithm that produces decision trees needs to choose what features to divide on… and then for each of those features, what values to use for the division.

生成决策树的机器学习算法需要选择要划分的特征… … 然后对于这些特征中的每一个，要使用什么值进行划分。

The next step is to create a model, and this involves selecting an algorithm to analyze the data. There are so many machine learning algorithms out there, such as decision trees, neural networks and so on.

下一步是创建模型，这涉及选择一种算法来分析数据。那里有很多机器学习算法，比如决策树、神经网络等等。

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。