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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 mnrfit
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。
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 glmfit
和 glmval
来拟合和计算广义线性模型。
使用 slicesample
对逻辑回归模型进行贝叶斯推断。
Train Logistic Regression Classifiers Using Classification Learner App
Create and compare logistic regression classifiers, and export trained models to make predictions for new data.
Wilkinson notation provides a way to describe regression and repeated measures models without specifying coefficient values.
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.
Hierarchical Multinomial Models
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.