Model Representation
In supervised learning, we have a data set and this data set is called a training set. The algorithm will learn from this data set and find pattern.
Training Set Of Housing Prices (Portland, OR).png
Notation:
- m = Number of training examples; 訓(xùn)練樣本的數(shù)目
- x's = 'input' variables / features; 特征量
- y's = 'output' variables / 'target' variables; 目標(biāo)變量
(x, y) : one training example; 一個(gè)訓(xùn)練樣本
( x (i), y (i)) : ith training example; i不是指數(shù)搬俊，而是指第i行的樣本
How supervised learning algorithm works
image.png
- Feed training set to learning algorithm.
- Learning algorithm output a function h(hypothesis).
- The function hypothesis takes in the value of x and tries to output the estimated value y.
- h is a function that maps from x's to y's.
Univariate linear regression : 一元線性回歸
(Univeriate is a fancy way of saying one variable)
In this case, x is the only variable.
