1.sigmoid function: 
logistic regression and linear regression function of contact
The compressed G z (z), between the (0,1), a collation 0.5 or more and 0.5 or less normalized class 0
2. logistic regression Features: good fit for linear effect, fast calculation, classification results are not fixed 0 and 1, but the probability of class numbers presented in decimal form.
3. Solving loss function: maximum likelihood estimate 
4. prevent overfitting: L1 regularization 
L2 regularization 
C control over the degree of regularization parameters, L1 parameter n will be compressed to 0, L2 parameters will be as small as only n
5. Regulation parameters loss function: penalty regular default L2
The default C 1, C is smaller stronger effect regularization
6. gradient descent important parameters: maximum number of iterations max_iter
7. Duality and multiple regression important parameters: solver selection method, the default liblinear coordinate descent, dichotomous
multi_class inform the type of treatment classification model, the default ovr binary, multinomial also fill multiple classifiers, auto automatic selection