mdl = fitrensemble(X, y)
yfit = predict(mdl, Xnew)
| Parameter | Description |
|---|---|
| X | numeric matrix: rows are observations and columns are predictors. |
| y | numeric vector: response values with one value for each row of X. |
| Name, Value | optional name-value arguments accepted by the corresponding fitting function. |
| Parameter | Description |
|---|---|
| mdl | regression model object returned by the fitting function. |
| yfit | predicted response values for new observations. |
RegressionEnsemble stores a regression model that combines multiple weak learners.
Create this object with fitrensemble. Use predict to aggregate learner responses for new observations.
X = [1 1; 2 1; 3 2; 4 3; 5 3; 6 4];
y = 1 + 2 * X(:,1) - X(:,2);
mdl = fitrensemble(X, y, 'NumLearningCycles', 3);
yfit = predict(mdl, [7 4; 8 5])| Version | Description |
|---|---|
| 2.0.0 | initial version |