# Classification and Representation

## Classification
* To attempt classification, one method is to use linear regression and map all predictions greater than 0.5 as a 1 and all less than a0.
* This method doesn't work well because classification is not actually a linear function.

## Hypothesis Representation
* Logistic Regression Model(函数g())
  * Sigmoid function
  * Logistic function
* `假设函数(0.7=P(y=1|x;theta)` --给定特性x,y等1的概率的参数是theta
# Classification and Representation

## Classification
* To attempt classification, one method is to use linear regression and map all predictions greater than 0.5 as a 1 and all less than a0.
* This method doesn't work well because classification is not actually a linear function.

## Hypothesis Representation
* Logistic Regression Model(函数g())
  * Sigmoid function
  * Logistic function
* `假设函数(0.7=P(y=1|x;theta)` --给定特性x,y等1的概率的参数是theta

## Decision Boundary
* 理解逻辑回归函数在计算什么
* 决策边界是函数的一个属性，函数的特性是属性的参数

## Non-linear decision boundaries
* 多项式回归，特征变量，得到复杂的决策边界
Add Decision Boundary and Non-linear decision boundaries
Add Decision Boundary and Non-linear decision boundaries
