Logistic Regression (逻辑回归)

Given $x \in R^{n_x}$, want $\hat{y}=P(y=1\,|\,x) \in [0,1]$

Logistic Function

$\sigma(z) = \dfrac{1}{1+e^{-z}}$

Suppose $w \in R^{n_x},\;b \in R$
$\hat{y}=\sigma ( w^{\tau}x + b )$

So $w$ and $b$ are the parameters we want to train

Logistic Regression Cost Function

$\hat{y}=\sigma ( w^{\tau}x + b )$, where $\sigma(z) = \dfrac{1}{1+e^{-z}}$
Given $\{(x^{(1)},y^{(1)}),(x^{(2)},y^{(2)}),\;…\;,(x^{(m)},y^{(m)})\}$, want $\hat{y}^{(i)} \approx y^{(i)}$

Mean squared error (均方误差)

$L(\hat{y}, y) = \dfrac{1}{2} (\hat{y} - y)^2$

We don’t use Mean squared error in Logistic Regression. Because that will make the cost function is no-convex.

Cross Entropy (交叉熵)

$L(\hat{y}, y) = -(ylog\hat{y} + (1-y)log(1-\hat{y}))$

Cost Function

$J(w,b) = \dfrac{1}{m} \sum\limits_{i=1}^{m} L(\hat{y}^{(i)}, y^{(i)}) = -\dfrac{1}{m} \sum\limits_{i=1}^{m} (y^{(i)}log\hat{y}^{(i)} + (1-y^{(i)})log(1-\hat{y}^{(i)}))$


[1] 逻辑回归(Logistic Regression)(一)
[2] 逻辑回归(Logistic Regression)(二)
[3] Logistic Regression 为什么用极大似然函数