一、基于原生Python实现岭回归(Ridge Regression) 岭回归(Ridge Regression)是一种常见的线性回归的扩展形式,它通过引入 L2正则化项 来解决线性回归模型中可能存在的过拟合问题。 在这里插入图片描述 线性回归模型的预测函数为: \hat{y} = \theta_0 + \theta_1 x_1 + \theta_2 x_2 + \cdots + \theta_...
y = data["hp"] # define cross-validation method to evaluate model cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1) # define model model = RidgeCV(alphas=arange(0, 1, 0.01), cv=cv, scoring='neg_mean_absolute_error') # fit model model.fit(X, y) # display lambda ...
the estimates. Larger values specify stronger regularization. Alpha corresponds to ``C^-1`` in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets.Hence they must correspond in number. max_iter : int, optional...
python from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split from sklearn.linear_model import Ridge from sklearn.metrics import mean_squared_error, r2_score import numpy as np # 生成模拟数据 X, y = make_regression(n_samples=100, n_features=10, noi...
ridge regression: 在最小二乘的基础上添加一个系数为α的惩罚项,惩罚项为参数向量2范数的平方,可以通过控制α来调节数据集的过拟合问题 拟合方法,参数调用与线性回归相同 岭回归优点:可以应用于高度坏条件矩阵(目标值的轻微改变会造成参数的大方差,数据曲线波动加剧,容易导致过拟合问题,因此添加系数为α的惩罚项减小...
L2正则化相比于L1正则化在计算梯度时更加简单。直接对损失函数关于w求导即可。这种基于L2正则化的回归模型便是著名的岭回归(Ridge Regression)。 Ridge 有了上一讲的代码框架,我们直接在原基础上对损失函数和梯度计算公式进行修改即可。下面来看具体代码。
.model_selectionimporttrain_test_splitX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42)fromnumpyimportarangefromsklearn.model_selectionimportGridSearchCVfromsklearn.model_selectionimportRepeatedKFoldfromsklearn.linear_modelimportLogisticRegressionmodel=LogisticRegression(...
fromsklearn.linear_modelimportLinearRegression regressor_linear = LinearRegression() regressor_linear.fit(X_train, y_train) 看看此时的预测得分 fromsklearn.metricsimportr2_score# 交叉验证:将数据集分为10折,做一次交叉验证,实际上它是计算了十次,将每一折都当做一次测试集,其余九折当做训练集,这样循环十次...
Python Implementation For this implementation, we will use the Boston housing dataset found in Sklearn. What we intend to see is: How to perform ridge and lasso regression in Python Compare the results with a linear regression model Data Importation and EDA #libraries import pandas as pd import...
前面一节我们学习了机器学习算法系列(三)- 标准线性回归算法(Standard Linear Regression Algorithm),最后求得标准线性回归的代价函数的解析解 w 为: