To interactively grow a classification tree, use the Classification Learner app. For greater flexibility, grow a classification tree using
fitctree at the command line. After growing a classification tree, predict labels by passing the tree and new predictor data to
|Classification Learner||Train models to classify data using supervised machine learning|
|ClassificationTree Predict||Classify observations using decision tree classifier (Since R2021a)|
Create Classification Tree
Improve Classification Tree
Interpret Classification Tree
|Local interpretable model-agnostic explanations (LIME) (Since R2020b)|
|Retrieve variable range of decision tree node (Since R2020a)|
|Compute partial dependence (Since R2020b)|
|Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots|
|Estimates of predictor importance for classification tree|
|Shapley values (Since R2021a)|
|Mean predictive measure of association for surrogate splits in classification tree|
|View classification tree|
Cross-Validate Classification Tree
|Cross-validated decision tree|
|Classification edge for cross-validated classification model|
|Classification loss for cross-validated classification model|
|Classification margins for cross-validated classification model|
|Classify observations in cross-validated classification model|
|Cross-validate function for classification|
|Classification error by resubstitution|
|Compare accuracies of two classification models using new data|
|Classification edge by resubstitution|
|Classification margins by resubstitution|
|Compare accuracies of two classification models by repeated cross-validation|
Gather Properties of Classification Tree
- Train Decision Trees Using Classification Learner App
Create and compare classification 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.
- Visualize Decision Surfaces of Different Classifiers
This example shows how to visualize the decision surface for different classification algorithms.
- Splitting Categorical Predictors in Classification Trees
Learn about the heuristic algorithms for optimally splitting categorical variables with many levels while growing decision trees.
- 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 Class Labels Using ClassificationTree Predict Block
Train a classification decision tree model using the Classification Learner app, and then use the ClassificationTree Predict block for label prediction.
- Human Activity Recognition Simulink Model for Fixed-Point Deployment
Generate code from a classification Simulink® model prepared for fixed-point deployment.
- Identify Punch and Flex Hand Gestures Using Machine Learning Algorithm on Arduino Hardware (Simulink Support Package for Arduino Hardware)
This example shows how to use the Simulink® Support Package for Arduino® Hardware to identify punch and flex hand gestures using a machine learning algorithm.