Logistic regression
- Linear regression problems one how to determine whether the tumor is malignant?
- linear regression robust enough, if there is noise, immediately, "surrender"
Logistic regression - Classification
Sigmoid function (compression function)
We will fit out the value of the linear regression compressed with a compression function, the compression is completed -
0.5 with a probability to make the decision boundary, the sample can be divided into two categories, i.e. positive samples
• sigmoid function, 
the determined value of the positive and negative z g (z) is greater than the last 0.5 or less than 0.5;
I.e. z is greater than 0, g (z) is greater than 0.5, when z is less than 0, g (z) is less than 0.5
• When the z expression is classified corresponding to a boundary, exactly corresponding to different sides of the boundary z negative classification,
It makes the classification boundary sides respectively g (z)> 0.5, and g (z) <0.5, so depending on the magnitude relation g (z) and 0.5,
Classification can be achieved
Logistic regression loss function
- the problem of quadratic loss function
In this way, we obtain a convex function.
Gradient descent method to solve
