广义线性模型

有关的例子 sklearn.linear_model module.

比较线性Bayesian回归量

Comparing Linear Bayesian Regressors

利用Bayesian Ridge回归进行曲线匹配

Curve Fitting with Bayesian Ridge Regression

多项和一对二回归的决策边界

Decision Boundaries of Multinomial and One-vs-Rest Logistic Regression

随机梯度下降的早停止

Early stopping of Stochastic Gradient Descent

使用预先计算的格拉姆矩阵和加权样本来匹配弹性网络

Fitting an Elastic Net with a precomputed Gram Matrix and Weighted Samples

HuberRegressor与Ridge在具有强异常值的数据集上

HuberRegressor vs Ridge on dataset with strong outliers

使用多任务Lasso进行联合特征选择

Joint feature selection with multi-task Lasso

逻辑回归中的L1罚分和稀疏性

L1 Penalty and Sparsity in Logistic Regression

基于L1的稀疏信号模型

L1-based models for Sparse Signals

通过信息标准选择Lasso模型

Lasso model selection via information criteria

Lasso型号选择：AIC-BIC /交叉验证

Lasso model selection: AIC-BIC / cross-validation

密集和稀疏数据上的套索

Lasso on dense and sparse data

Lasso、Lasso-LARS和Elastic Net路径

Lasso, Lasso-LARS, and Elastic Net paths

逻辑函数

Logistic function

使用多项逻辑+ L1的MNIST分类

MNIST classification using multinomial logistic + L1

20个新群体的多类稀疏逻辑回归

Multiclass sparse logistic regression on 20newgroups

非负最小平方

Non-negative least squares

使用随机梯度下降的一类支持者与一类支持者

One-Class SVM versus One-Class SVM using Stochastic Gradient Descent

普通最小二乘和岭回归

Ordinary Least Squares and Ridge Regression

正交匹配追踪

Orthogonal Matching Pursuit

绘制岭系数作为正规化的函数

Plot Ridge coefficients as a function of the regularization

在iris数据集上绘制多类新元

Plot multi-class SGD on the iris dataset

多项和样条插值

Polynomial and Spline interpolation

分位数回归

Quantile regression

L1-逻辑回归的正规化路径

Regularization path of L1- Logistic Regression

作为L2正则化函数的脊系数

Ridge coefficients as a function of the L2 Regularization

鲁棒线性估计量匹配

Robust linear estimator fitting

使用RASAC的鲁棒线性模型估计

Robust linear model estimation using RANSAC

新元：分离超平面的最大裕度

SGD: Maximum margin separating hyperplane

新元：处罚

SGD: Penalties

新元：加权样本

SGD: Weighted samples

新元：凸损失函数

SGD: convex loss functions

Theil-Sen回归

Theil-Sen Regression