广义线性回归
具有各种分布和联系函数的广义线性回归模型,包括逻辑回归
为了提高在中低维数据集上的准确度并增加联系函数选择,可以使用 fitglm
拟合广义线性回归模型。对于多项式逻辑回归,使用 fitmnr
拟合模型。
为了减少在高维数据集上的计算时间,可以使用 fitclinear
训练二类线性分类模型,例如逻辑回归模型。还可以使用 fitcecoc
高效地训练由逻辑回归模型组成的多类纠错输出编码 (ECOC) 模型。
对于大数据的非线性分类,可以使用 fitckernel
训练带逻辑回归的二类高斯核分类模型。
模块
ClassificationLinear Predict | Classify observations using linear classification model (自 R2023a 起) |
函数
对象
主题
广义线性回归
- Generalized Linear Models
Generalized linear models use linear methods to describe a potentially nonlinear relationship between predictor terms and a response variable. - Generalized Linear Model Workflow
Fit a generalized linear model and analyze the results. - 使用广义线性模型拟合数据
使用glmfit
和glmval
来拟合和计算广义线性模型。 - Train Binary GLM Logistic Regression Classifier Using Classification Learner App
Create and compare binary logistic regression classifiers, and export trained models to make predictions for new data. - Predict Class Labels Using ClassificationLinear Predict Block
This example shows how to use the ClassificationLinear Predict block for label prediction in Simulink®. (自 R2023a 起) - Wilkinson Notation
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.
多项式逻辑回归
- Multinomial Models for Nominal Responses
A nominal response variable has a restricted set of possible values with no natural order between them. A nominal response model explains and predicts the probability that an observation is in each category of a categorical response variable. - Multinomial Models for Ordinal Responses
An ordinal response variable has a restricted set of possible values that fall into a natural order. An ordinal response model describes the relationship between the cumulative probabilities of the categories and predictor variables. - Multinomial Models for Hierarchical Responses
A hierarchical multinomial response variable (also known as a sequential or nested multinomial response) has a restricted set of possible values that fall into hierarchical categories. The hierarchical multinomial regression models are extensions of binary regression models based on conditional binary observations.