Coursera Machine Learning 第二周 quiz Linear Regression with Multiple Variables 习题答案
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1.Suppose m=4 students have taken some class, and the class had a midterm exam and a final exam. You have collected a dataset of their scores on the two exams, which is as follows:
midterm exam(midterm exam)^2final exam89792196725184749488368769476178You'd like to use polynomial regression to predict a student's final exam score from their midterm exam score. Concretely, suppose you want to fit a model of the form
What is the normalized feature
2.You run gradient descent for 15 iterations
with
value of
off. Based on this, which of the following conclusions seems
most plausible?
Rather than use the current value of
Rather than use the current value of
下降太快所以a下降速率过大,a越大下降越快,a小下降慢,而本题中,代价函数快速收敛到最小值,代表此时a最合适
Suppose you have
此题注意,计算X维度时要加上X0=[1,1,1,1..,1];故答案选C
4
Suppose you have a dataset with
Gradient descent, since it will always converge to the optimal
Gradient descent, since
The normal equation, since it provides an efficient way to directly find the solution.
The normal equation, since gradient descent might be unable to find the optimal
训练集有2W,转置的话需要大量的时间,花费太多内存与资源,故选择梯度下降。选择B
Which of the following are reasons for using feature scaling?
It speeds up gradient descent by making it require fewer iterations to get to a good solution.
It speeds up solving for
It is necessary to prevent gradient descent from getting stuck in local optima.
It prevents the matrix
迭代次数的减少,加快了正确答案的得出。正规方程对计算只与训练集的大小有关,而与至无关,不能阻止梯度下降局部最优(ps:正规方程没有局部最优),第四个答案,除非可以减少特征变量,否则不能解决此问题所以选A
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