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Classification Trees

Binary decision trees for multiclass learning

To interactively grow a classification tree, use the Classification Learner app. For greater flexibility, grow a classification tree using fitctree in the command-line interface. After growing a classification tree, predict labels by passing the tree and new predictor data to predict.


Classification Learner Train models to classify data using supervised machine learning


fitctree Fit binary classification decision tree for multiclass classification
predict Predict labels using classification tree
templateTree Create decision tree template


ClassificationTree Binary decision tree for classification
CompactClassificationTree Compact classification tree
ClassificationPartitionedModel Cross-validated classification model

Examples and How To

Train Decision Trees Using Classification Learner App

Learn how to train classification trees.

Train Classification Tree

Train a classification tree using sample data.

Prediction Using Classification and Regression Trees

Predict class labels or responses using trained classification and regression trees.

Improving Classification Trees and Regression Trees

Tune trees by setting name-value pairs in fitctree and fitrtree.


What Are Decision Trees?

Decision trees predict responses to data based on a sequence of decisions.

Splitting Categorical Predictors

When growing a classification tree, finding an optimal binary split for a categorical predictor with many levels is significantly more computationally challenging than finding a split for a continuous predictor. Several heuristic algorithms for this task are available.

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