本页对应的英文页面已更新,但尚未翻译。 若要查看最新内容,请点击此处访问英文页面。
要以交互方式生成回归树,可以使用 Regression Learner App。为了获得更大的灵活性,可以在命令行中使用 fitrtree
生成回归树。生成回归树后,可将树和新的预测变量数据传递给 predict
,以预测响应。
Regression Learner | Train regression models to predict data using supervised machine learning |
RegressionTree | Regression tree |
CompactRegressionTree | Compact regression tree |
RegressionPartitionedModel | Cross-validated regression model |
Train Regression Trees Using Regression Learner App
Create and compare regression trees, and export trained models to make predictions for new data.
Supervised Learning Workflow and Algorithms
Understand the steps for supervised learning and the characteristics of nonparametric classification and regression functions.
Understand decision trees and how to fit them to data.
To grow decision trees, fitctree
and
fitrtree
apply the standard CART algorithm by default to
the training data.
Create and view a text or graphic description of a trained decision tree.
Improving Classification Trees and Regression Trees
Tune trees by setting name-value pair arguments in
fitctree
and fitrtree
.
Prediction Using Classification and Regression Trees
Predict class labels or responses using trained classification and regression trees.
Predict Out-of-Sample Responses of Subtrees
Predict responses for new data using a trained regression tree, and then plot the results.