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)|
|Retrieve variable range of decision tree node|
|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
- 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.
- Decision Trees
Understand decision trees and how to fit them to data.
- Growing Decision Trees
To grow decision trees,
fitrtreeapply the standard CART algorithm by default to the training data.
- View Decision Tree
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
- 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.
- Predict Responses Using RegressionTree Predict Block
This example shows how to use the RegressionTree Predict block for response prediction in Simulink®.