【机器学习】-Week2 1. 多变量线性回归
Multiple Features
Note:
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Linear regression with multiple variables is also known as "multivariate linear regression".
We now introduce notation for equations where we can have any number of input variables.
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The multivariable form of the hypothesis function accommodating these multiple features is as follows:
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In order to develop intuition about this function, we can think about θ0 as the basic price of a house, θ1 as the price per square meter, θ2 as the price per floor, etc. x_1 will be the number of square meters in the house, x_2 the number of floors, etc.
Using the definition of matrix multiplication, our multivariable hypothesis function can be concisely represented as:
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This is a vectorization of our hypothesis function for one training example; see the lessons on vectorization to learn more.
Remark: Note that for convenience reasons in this course we assume
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This allows us to do matrix operations with theta and x. Hence making the two vectors 'θ' and x_i match each other element-wise (that is, have the same number of elements: n+1)]
来源:coursera 斯坦福 吴恩达 机器学习