监督学习和无监督学习的定义及解释（举例说明，便于理解）（详细）

监督学习被分为两类：

(a) Regression - Given a picture of a person, we have to predict their age on the basis of the given picture

​ （a）回归-给定一个人的照片，我们必须根据给定的照片来预测他们的年龄。

(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

​ （b）分类-给一个患有肿瘤的患者，我们必须预测该肿瘤是恶性还是良性的。

在监督学习中，我们得到了一个数据集，并且已经知道我们正确的输出应该是什么样子，并且认为输入和输出之间存在关系。

In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.

Supervised learning problems are categorized into “regression” and “classification” problems. In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function. In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.

监督学习问题分为“回归”和“分类”问题。在回归问题中，我们试图预测连续输出中的结果，这意味着我们试图将输入变量映射到某个连续函数。在分类问题中，我们改为尝试预测离散输出中的结果。换句话说，我们正在尝试将输入变量映射为离散类别。

Example 1:

Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.

We could turn this example into a classification problem by instead making our output about whether the house “sells for more or less than the asking price.” Here we are classifying the houses based on price into two discrete categories.

示例1：根据有关房地产市场上房屋大小的数据，尝试预测其价格。价格作为规模的函数是一个连续的输出，因此这是一个回归问题。
我们可以通过输出有关房屋是否“以高于或低于要价的价格出售”的输出，从而将这个示例转变为分类问题。在这里，我们将根据价格将房屋分为两类。

Example 2:

(a) Regression - Given a picture of a person, we have to predict their age on the basis of the given picture

(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

示例2：（a）回归-给定一个人的照片，我们必须根据给定的照片来预测他们的年龄（b）分类-给定一个患有肿瘤的患者，我们必须预测该肿瘤是恶性还是恶性良性。

Example 2:

(a) Regression - Given a picture of a person, we have to predict their age on the basis of the given picture

(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

您将使用无监督学习算法解决哪些问题？

• Given a set of news articles found on the web, group them into sets of articles about the same stories.

  • 给定在网络上找到的一组新闻文章，将它们分组为关于相同故事的文章。

• Given a database of customer data, automatically discover market segments and group customers into different market segments.

  • 给定客户数据数据库，可以自动发现市场细分并将客户分组到不同的市场细分中。

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.

We can derive this structure by clustering the data based on relationships among the variables in the data.

With unsupervised learning there is no feedback based on the prediction results.

Example:

Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.

Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).

无监督的学习使我们几乎不了解结果应该是什么样子就可以解决问题。我们可以从数据中获得结构，而不必知道变量的影响。
我们可以通过基于数据中变量之间的关系对数据进行聚类来推导此结构。
在无监督学习的情况下，没有基于预测结果的反馈。

示例：聚类：收集1,000,000个不同基因的集合，然后找到一种方法，将这些基因自动分组为通过不同变量（例如寿命，位置，角色等）在某种程度上相似或相关的组。
非集群：“鸡尾酒会算法”，使您可以在混乱的环境中找到结构。 （即在鸡尾酒会上从一连串的声音中识别出个人的声音和音乐）。