To interactively grow a regression tree, use the Regression Learner app. For greater flexibility, grow a regression tree using
fitrtree at the command line. After growing a regression tree, predict responses by passing the tree and new predictor data to
|Regression Learner||Train regression models to predict data using supervised machine learning|
|RegressionTree Predict||Predict responses using regression tree model|
Create Regression Tree
Interpret Regression Tree
|Local interpretable model-agnostic explanations (LIME)|
|Compute partial dependence|
|Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots|
|Estimates of predictor importance for regression tree|
|Mean predictive measure of association for surrogate splits in regression tree|
|View regression tree|
Cross-Validate Regression Tree
Gather Properties of Regression Tree
Create and compare regression trees, and export trained models to make predictions for new data.
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,
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.
Tune trees by setting name-value pair arguments in
Predict class labels or responses using trained classification and regression trees.
Predict responses for new data using a trained regression tree, and then plot the results.
This example shows how to use the RegressionTree Predict block for response prediction in Simulink®.