回归
回归models describe the relationship between a response (output) variable, and one or more predictor (input) variables. Statistics and Machine Learning Toolbox™ allows you to fit linear, generalized linear, and nonlinear regression models, including stepwise models and mixed-effects models. Once you fit a model, you can use it to predict or simulate responses, assess the model fit using hypothesis tests, or use plots to visualize diagnostics, residuals, and interaction effects.
Statistics and Machine Learning Toolbox also provides nonparametric regression methods to accommodate more complex regression curves without specifying the relationship between the response and the predictors with a predetermined regression function. You can predict responses for new data using the trained model. Gaussian process regression models also enable you to compute prediction intervals.
Categories
- 回归Learner App
交互式训练,验证和调节回归模型 - Linear Regression
Multiple, stepwise, multivariate regression models, and more - Generalized Linear Models
Logistic regression, multinomial regression, Poisson regression, and more - Nonlinear Regression
Nonlinear fixed- and mixed-effects regression models - Support Vector Machine Regression
Support vector machines for regression models - 高斯过程回归
高斯流程回归模型(Kriging) - 回归Trees
Binary decision trees for regression - 回归树合奏
随机森林,增强和行李的回归树 - Generalized Additive Model
Interpretable model composed of univariate and bivariate shape functions for regression - 神经网络
Neural networks for regression - Incremental Learning
Fit linear model for regression to streaming data and track its performance - 解释性
Train interpretable regression models and interpret complex regression models - Model Building and Assessment
特征选择,功能工程,模型选择,超参数优化,交叉验证,残留诊断和图