Overview
Logistic Regression
- Classification and Representation
- Classification
- Hypothesis Representation
- Decision Boundary
- Logistic Regression Model
- Cost Function
- Simplified Cost Function and Gradient Descent
- Advanced Optimization
- Multiclass Classification
- Multiclass Classification: One-vs-all
- Review
Log
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9/20: watched videos in 3.1
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9/21: watched videos in 3.2, 3.3
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第一次quiz没通过,3.2需要复习;
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9/23: 过了一遍3.4视频,但没太听进去,需要重听;
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2/15/2017: programming assignment done;
Reading
Note
3.1 Classification and Representation
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Logistic Regression is actually a classification algorithm.
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sigmoid function or logistic function
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Interpretation of Hypothesis Output
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h(x) = estimated probability that y = 1 on input x
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h(x) = p(y|x; theta), probability that y = 1 given x, parameterized by theta
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Decision Boundaries
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Linear Decision Boundaries
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Non-linear Decision Boundaries
3.2 Logistic Regression Model
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Logistic regression cost function
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simplified cost function
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Gradient Descent
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Optimization algorithm
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Gradient descent
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Conjugate gradient
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BFGS
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L-BFGS
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Ad: no need to manually pick alpha; often faster than gradient descent
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Disad: more complex
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3.3 Multiclass Classification
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one-vs-all (one-vs-rest)
3.4 Solving the Problem of Overfitting (新版这部分已删除?)
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The problem of Overfitting – 过度拟合(Overfitting)
- 例子:对一组适合二次多项式拟合的数据
- 用一次多项式拟合,会造成underfit,high bias
- 用四次多项式拟合,会造成overfit,high variance
- 过度拟合的后果:可以很好地拟合现有数据(效用函数接近零),但缺乏新数据的预测能力
- 如何防止
- 减少feature:手动选择/依赖算法
- 正则化
- 例子:对一组适合二次多项式拟合的数据
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Cost function – 效用/代价函数
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线性回归的效用函数
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应用到hθ(x)效用函数非凸,会有很多局部极小存在,梯度下降不一定能得到全局最优
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逻辑回归的效用函数
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选用以上效用函数后,梯度下降法具体操作同前
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另外还有共轭梯度conjugate gradient、BFGS、L-BFGS:不需指定步长,更快,但更复杂
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Regularization – 正则化
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在效用函数上添加对feature的惩罚项(平方和项)来达到减小feature值的目的
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若不知道需要减小哪些feature项就所有项都加一个平方和(除去常数系数项之外)
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若惩罚过重,拟合函数会变一条常数直线,underfitting
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正规化可以用于梯度下降方法和逻辑回归方法,修改效用函数后,其他步骤同前
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Regularized Linear Regression – 正则化线性回归
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Regularized Logistic Regression – 正则化逻辑回归