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今天小編為大家帶來了解監督學習中的線性回歸演算法，
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Today, the editor brings you the "Learn about reinforcement learning in machine learning"，
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線性回歸演算法是一種用於建立因變量與一個或多個自變量之間線性關系的監督學習演算法。
A linear regression algorithm is a supervised learning algorithm used to establish a linear relationship between a dependent variable and one or more independent variables.
一、基本原理
First, the rationale
1. 假設因變量與自變量之間存線上性關系，可以用一個線性方程式來表示，例如對於一元線性回歸，方程式為 y=wx+b ，其中 y 是因變量, x 是自變量, w 是權重系數, b 是偏置項。
1. It is assumed that there is a linear relationship between the dependent variable and the independent variable, which can be expressed by a linear equation, for example, for univariate linear regression, the equation is y=wx+b, where y is the dependent variable, x is the independent variable, w is the weight coefficient, and b is the bias term.
2. 透過最小化實際值與預測值之間的誤差來確定最優的權重系數和偏置項。常用的誤差度量方法是均方誤差（MSE）。
2. Determine the optimal weight coefficient and bias term by minimizing the error between the actual value and the predicted value. A commonly used measure of error is the mean square error (MSE).
二、求解方法
Second, the solution method
1. 最小平方法：透過求解誤差函式關於權重系數和偏置項的偏導數為零的方程式組，得到最優解。對於一元線性回歸，可以直接得到解析解；對於多元線性回歸，通常使用矩陣運算求解。
1. Least Squares: The optimal solution is obtained by solving a system of equations with zero partial derivatives of the error function with respect to the weight coefficient and the bias term. For univariate linear regression, the analytical solution can be obtained directly; For multiple linear regression, matrix operations are usually used to solve the problem.
2. 梯度下降法：一種叠代最佳化演算法，透過不斷調整權重系數和偏置項，使得誤差函式逐漸減小。每次叠代沿著誤差函式的負梯度方向更新參數。
2. Gradient descent method: An iterative optimization algorithm that gradually decreases the error function by continuously adjusting the weight coefficient and bias term. Each iteration updates the parameters in the direction of the negative gradient of the error function.
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參考資料：chatGPT3.5轉譯、百度
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