英文;

Supervised Learning

　　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.

　　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.

　　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.

下面是個人理解，在監督學習中，我們有一個數據集包括正確的輸出，確信在輸入和輸出之間有一個關系。監督學習分為回歸問題和分類問題。

分類問題 預測離散值輸出 能處理無窮多的特征，將結果集進行分類

回歸問題 預測連續值輸出 目的是給出一系列的正確答案，例如給出房子的估價。病人腫瘤是良性還是惡性也可以用回歸問題解決，分類能夠給出正確的離散值判斷腫瘤是良性的還是惡性的，離散值是0還是1。若腫瘤的還有其他type，我們也要預測出更多的離散值。

吳恩達機器學習筆記2-監督學習