mdl = fitcensemble(X, Y)
label = predict(mdl, Xnew)
[label, score] = predict(mdl, Xnew)
| Parameter | Description |
|---|---|
| X | numeric matrix: rows are observations and columns are predictors. |
| Y | vector: class labels with one label for each row of X. |
| Name, Value | optional name-value arguments accepted by the corresponding fitting function. |
| Parameter | Description |
|---|---|
| mdl | classification model object returned by the fitting function. |
| label | predicted class labels for new observations. |
| score | class scores or posterior-like values when the model provides them. |
ClassificationEnsemble stores a classification model that combines multiple weak learners.
Create this object with fitcensemble. Use predict to aggregate learner responses for new observations.
X = [0 0; 0 1; 1 0; 5 5; 5 6; 6 5];
Y = [1; 1; 1; 2; 2; 2];
mdl = fitcensemble(X, Y, 'NumLearningCycles', 3);
label = predict(mdl, [0.2 0.1; 5.2 5.1])| Version | Description |
|---|---|
| 2.0.0 | initial version |