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今天小编为大家带来了解监督学习中的线性回归算法，
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Today, the editor brings you the "Learn about reinforcement learning in machine learning"，
welcome to visit !
线性回归算法是一种用于建立因变量与一个或多个自变量之间线性关系的监督学习算法。
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. 假设因变量与自变量之间存在线性关系，可以用一个线性方程来表示，例如对于一元线性回归，方程为 y=wx+b ，其中 y 是因变量, x 是自变量, w 是权重系数, b 是偏置项。
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. 通过最小化实际值与预测值之间的误差来确定最优的权重系数和偏置项。常用的误差度量方法是均方误差（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. 最小二乘法：通过求解误差函数关于权重系数和偏置项的偏导数为零的方程组，得到最优解。对于一元线性回归，可以直接得到解析解；对于多元线性回归，通常使用矩阵运算求解。
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：一种迭代优化算法，通过不断调整权重系数和偏置项，使得误差函数逐渐减小。每次迭代沿着误差函数的负梯度方向更新参数。
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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参考资料：chatGPT3.5翻译、百度
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