This is the homework from CS5651 Machine Learning in National Tsing Hua University.
Implenment Linear Regression to predict $\hat{y}$ by Tensorflow. The dataset and label are given and we have to split the data to two parts: training and testing. Head 90% are for training dataset, the others are testing dataset. After prediction, calculate the error rate to examine its performance.
$\hat{y}$ is the value we predict for y. We define the ourput is
$$\hat{y}=XW+b ...(1)$$
X is a $n\cdot m$ input matrix. (n: number of samples, m: number of features)
W is a $m\cdot 1$ matrix, which is the linear regression parameters that we want.
Modify equation (1) to
$$\hat{y}=XW ...(2)$$
by augmenting X to n*(m+1) matrix. The last column are all one, and W will also become (m+1)*1 matrix. The last element of W achieve the same result as b.
Next, apply normal function to derive W.
$$W=(X^{(train)T}X^{(train)})^{-1}X^{(train)T}y^{(train)}$$
Do normalization and take logarithm
$$New data=log[(data-min+1)/(max-min)]$$
"Plus 1" is because of avoiding 0 value in logarithm.
For error rate, it is the average of $\frac{|y-\hat{y}|}{y}$
Without data preprocessing, the error rate of testing part is 0.344205405993.
With data preprocessing, the error rate is 0.314443644703.
