Lecture 5: Logistic Regression
Lecture 5:
Logistic Regression
Shuai Li
John Hopcroft Center, Shanghai Jiao Tong University
1
Outline
? Discriminative / Generative Models
? Logistic regression (binary classification)
? Cross entropy
? Formulation, sigmoid function
? Training¡ªgradient descent
? More measures for binary classification (AUC, AUPR)
? Class imbalance
? Multi-class logistic regression
2
Discriminative / Generative
Models
3
Discriminative / Generative Models
? Discriminative models
?
?
?
?
Modeling the dependence of unobserved variables on observed ones
also called conditional models.
Deterministic:
Probabilistic:
? Generative models
? Modeling the joint probabilistic distribution of data
? Given some hidden parameters or variables
? Then do the conditional inference
4
Discriminative Models
? Discriminative models
?
?
?
?
Modeling the dependence of unobserved variables on observed ones
also called conditional models.
Deterministic:
Probabilistic:
?
?
?
?
Directly model the dependence for label prediction
Easy to define dependence on specific features and models
Practically yielding higher prediction performance
E.g. linear regression, logistic regression, k nearest neighbor, SVMs, (multilayer) perceptrons, decision trees, random forest
5
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