[机器学习02]supervised learning and unsupervised learning

1.Housing price prediction

图中坐标系横轴表示房子面积，纵轴表示房子价格，已收集到一些房屋面积和价格的数据，在图中用红色X标记。现在预测房屋面积为750英尺，价格为多少？可以根据点集进行拟合，根据拟合后的函数求解面积为750英尺时的房屋价格.

2.Cancer prediction

3.supervised learning

监督学习的特点是训练集已给出，通过已有的训练样本（即已知数据以及对应的输出）去训练得到一个最优模型（这个模型属于某个函数的集合，最优则表示在某个评价标准下是最佳的）,再利用这个模型将所有的输入映射为相应的输出，从而实现分类的目的，也就是有了对未知数据进行分类的能力.
example：
You’re running a company, and you want to develop learning algorithms to address each of two problems.

Problem 1: You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months.
Problem 2: You’d like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised.
Should you treat these as classification or as regression problems?

• Treat both as classification problems.
• Treat problem 1 as a classification problem, problem 2 as a regression problem.
• Treat problem 1 as a regression problem, problem 2 as a classification problem.
• Treat both as regression problems.

4.unsupervised learning

在无监督学习中，数据集的特征是不知道的，没有任何训练样本。比如在实际生活中，我们从小被大人教育什么是猫、什么是树，我们在潜意识中记住了猫、树的特征，以后我们就会根据我们脑海中存在的特征（已知训练样本）去辨认哪些是猫、哪些是树，这就是典型的分类；而无监督学习就好比去参观一次画展，我们事先并不知道画展中有哪些作品（未知训练集），当参观完以后我们根据参观的过程中的认识（自主学习），会自动划分哪些作品是山水画、哪些作品是书法作品（聚类）.

聚类的应用举例：

1. 比如在百度中Q:习近平访英
   
   绿框标志的新闻来自不同的新闻网站，但都有同一个主题，通过分类算法和无监督学习，搜索引擎将具有相同主题的网页链接聚类在一起，反馈给用户更好的搜索结果。
2. 社交网络中人群分析
3. 市场用户划分
4. 超大规模行星数据分析

一个区分监督学习和无监督学习的例子：
Of the following examples, which would you address using an unsupervised learning algorithm? (Check all that apply.)

• Given a database of customer data, automatically discover market segments and group customers into different market segments.(supervised)
• Given email labeled as spam/not spam, learn a spam filter.(unsupervised)
• Given a set of news articles found on the web, group them into set of articles about the same story.(unsupervised)
• Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.(supervised)