深入浅出机器学习-线性回归Linear regression 线性回归: 1.函数模型(Model): 假设有训练数据 那么为了方便我们写成矩阵的形式 2.损失函数(cost): 现在我们需要根据给定的X求解W的值,这里采用最小二乘法。 a.最小二乘法: 我们有很多的给定点,这时候我们需要找出一条线去拟合它,那么我先假设这个线的方程,然后把数据点代入假设的
In machine learning, linear regression uses a linear equation to model the relationship between a dependent variable (Y) and one or more independent variables (Y).The main goal of the linear regression model is to find the best-fitting straight line (often called a regression line) through a...
learning algorithm -> f x−>f−>ˆyx:featuref:modelˆy:prediction(estimatedy)x−>f−>y^x:featuref:modely^:prediction(estimatedy) represent f fw,b(x)=wx+bfw,b(x)=wx+b linear regression with one variable. example:
This paper describes various Supervised Machine Learning classification techniques, compares various supervised learning algorithms as well as determines the most efficient classification algorithm based on the data set, the number of instances and variables (features). A simple linear regression model is ...
from sklearn.linear_model import LinearRegression regressor=LinearRegression() regressor.fit(x_train,y_train) After fitting in the linear regression function. This is how we get the predicted values of brain weight using linear regression:
三、Robust regression鲁棒线性回归(Laplace/Student似然+均匀先验) 因为先验服从均匀分布,所以求鲁棒线性回归即求Laplace/Student最大似然。在heavy tail(奇异点较多)情况下用鲁棒线性回归,因为Laplace/Student分布比高斯分布更鲁棒。 似然函数为: 由于零点不可微,所以求解析解困难,无法使用梯度下降法。引入Huber损失函数解...
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LinearRegression sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False,copy_X=True, n_jobs=1) 参数: 1、fit_intercept:boolean,optional,default True。是否计算截距,默认为计算。如…
Run each value of the x array through the function. This will result in a new array with new values for the y-axis: mymodel =list(map(myfunc, x)) Draw the original scatter plot: plt.scatter(x, y) Draw the line of linear regression: ...
一元线性回归(Simple Linear Regression): 假设只有一个自变量x(independent variable,也可称为输入input, 特征feature),其与因变量y(dependent variable,也可称为响应response, 目标target)之间呈线性关系,当然x和y之间不会完全是直线关系,而是会有一些波动(因为在现实中,不一定只有一个自变量x会影响因变量y,可能还会...