【Python机器学习】Supervised learning 监督学习

It refers to algorithms that learn X to Y or input to output mappings.The key characteristic of supervised learning is that you give your learning algorithm examples to learn from.That includes the right answers which means the correct label Y for a given input X,and is by seeing correct pairs of input X and desired output label Y that the learning algorithm eventually learns to take just the input alone without the output label and gives a reasonably accurate prediction or guess of the output.

它指的是学习X到Y或输入到输出映射的算法。监督学习的关键特点是你给你的学习算法提供了学习的例子。这包括正确的答案，即给定输入X的正确标签Y，并且通过看到正确的输入X和期望的输出标签Y对，学习算法最终学会只接受没有输出标签的输入，并给出对输出的合理准确的预测或猜测。

Example:

Input(X)	Output(Y)	Application
email	spam?(0/1)	spam filtering
audio	text transcripts	speech recognition
English	Spanish	machine translation
ad,user info	click?(0/1)	online advertising
image,radar info	position of other cars	self-driving car
image of phone	defect?(0/1)	visual inspection

Regression 回归

Linear Regression

model:
$$f_{w,b}(x)=wx+b$$
parameters:
$$w,b$$
cost function:
J ( w , b ) = 1 2 m ∑ i = 1 m ( f w , b ( x ( i ) ) − y ( i ) ) 2 J(w,b)=\frac{1}{2m}\sum^{m}_{i=1}{(f_{w,b}(x^{(i)})-y^{(i)})^2} J(w,b)=2m1​i=1∑m​(fw,b​(x(i))−y(i))<