weixin_43451186的博客

scikit-learn 之岭回归基本语句from sklearn import linear_modelreg = linear_model.Ridge(alpha = 0.5)reg.fit([[0,0],[1,1],[2,2]],[0,1,2])reg.coef_ #coeficientsreg.intercept_Plot Ridge coefficients as a f...

lasso回归基本语法from sklearn import linear_modelreg = linear_model.Lasso(alpha=0.1)reg.fit([[0,0],[1,1],[2,2]],[0,1,2])reg.coef_ #coeficientsreg.intercept_reg.predict([[1,1]])和之前的线性回归之类的都很像哦~Lass...

from itertools import cyclefrom sklearn.linear_model import lasso_path,enet_pathfrom sklearn import datasetsimport numpy as npimport matplotlib.pyplot as plt diabetes = datasets.load_diabetes()...

scikit learn 之OLS最近刚刚开始学习Python，之前是对R语言进行的机器学习比较熟悉了，所以算法的数学推导还算理解，主要是针对于Python中的函数进行了学习嘿嘿~希望自己能够输出带动学习！！代码from sklearn import linear_modelreg = linear_model.LinearRegression()reg.fit([[0,0],[1,1]...

L1 Penalty and Sparsity in Logistic Regression#比较不同值C在L1和L2惩罚下解的稀疏性(零系数百分比)。可以看出，C值越大，模型的自由度越大。#相反，C的值越小，对模型的约束越大。在L1惩罚的情况下，这会导致稀疏解。#我们将8x8的数字图像分为两类:0-4和5-9。可视化显示了C变化模型的系数import numpy as npimpor...

1.2. Linear and Quadratic Discriminant AnalysisLinear and Quadratic Discriminant Analysis with covariance ellipsoidfrom scipy import linalgimport numpy as npimport matplotlib.pyplot as pltimport...