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|
|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 classification tree|
|Mean predictive measure of association for surrogate splits in classification tree|
|View 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|
Create and compare classification 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.
This example shows how to visualize the decision surface for different classification algorithms.
Learn about the heuristic algorithms for optimally splitting categorical variables with many levels while growing decision trees.
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.
Train a classification decision tree model using the Classification Learner app, and then use the ClassificationTree Predict block for label prediction.
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.