Documentation |
RegressionEnsemble combines a set of trained weak learner models and data on which these learners were trained. It can predict ensemble response for new data by aggregating predictions from its weak learners.
ens = fitensemble(X,Y,method,nlearn,learners) returns an ensemble model that can predict responses to data. The ensemble consists of models listed in learners. For more information on the syntax, see the fitensemble function reference page.
ens = fitensemble(X,Y,method,nlearn,learners,Name,Value) returns an ensemble model with additional options specified by one or more Name,Value pair arguments. For more information on the syntax, see the fitensemble function reference page.
CategoricalPredictors |
List of categorical predictors. CategoricalPredictors is a numeric vector with indices from 1 to p, where p is the number of columns of X. |
CombineWeights |
A string describing how the ensemble combines learner predictions. |
FitInfo |
A numeric array of fit information. The FitInfoDescription property describes the content of this array. |
FitInfoDescription |
String describing the meaning of the FitInfo array. |
LearnerNames |
Cell array of strings with names of the weak learners in the ensemble. The name of each learner appears just once. For example, if you have an ensemble of 100 trees, LearnerNames is {'Tree'}. |
Method |
A string with the name of the algorithm fitensemble used for training the ensemble. |
ModelParameters |
Parameters used in training ens. |
NumObservations |
Numeric scalar containing the number of observations in the training data. |
NumTrained |
Number of trained learners in the ensemble, a positive scalar. |
PredictorNames |
A cell array of names for the predictor variables, in the order in which they appear in X. |
ReasonForTermination |
A string describing the reason fitensemble stopped adding weak learners to the ensemble. |
Regularization |
A structure containing the result of the regularize method. Use Regularization with shrink to lower resubstitution error and shrink the ensemble. |
ResponseName |
A string with the name of the response variable Y. |
ResponseTransform |
Function handle for transforming scores, or string representing a built-in transformation function. 'none' means no transformation; equivalently, 'none' means @(x)x. Add or change a ResponseTransform function using dot notation: ens.ResponseTransform = @function |
Trained |
The trained learners, a cell array of compact regression models. |
TrainedWeights |
A numeric vector of weights the ensemble assigns to its learners. The ensemble computes predicted response by aggregating weighted predictions from its learners. |
W |
The scaled weights, a vector with length n, the number of rows in X. The sum of the elements of W is 1. |
X |
The matrix of predictor values that trained the ensemble. Each column of X represents one variable, and each row represents one observation. |
Y |
The numeric column vector with the same number of rows as X that trained the ensemble. Each entry in Y is the response to the data in the corresponding row of X. |
compact | Create compact regression ensemble |
crossval | Cross validate ensemble |
cvshrink | Cross validate shrinking (pruning) ensemble |
regularize | Find weights to minimize resubstitution error plus penalty term |
resubLoss | Regression error by resubstitution |
resubPredict | Predict response of ensemble by resubstitution |
resume | Resume training ensemble |
shrink | Prune ensemble |
loss | Regression error |
predict | Predict response of ensemble |
predictorImportance | Estimates of predictor importance |
removeLearners | Remove members of compact regression ensemble |
Value. To learn how value classes affect copy operations, see Copying Objects in the MATLAB^{®} documentation.
Create a boosted regression ensemble to predict the mileage of cars in the carsmall data set based on their weights and numbers of cylinders:
load carsmall learner = templateTree('MinParent',20); ens = fitensemble([Weight, Cylinders],MPG,... 'LSBoost',100,learner,'PredictorNames',{'W','C'},... 'categoricalpredictors',2) ens = classreg.learning.regr.RegressionEnsemble: PredictorNames: {'W' 'C'} CategoricalPredictors: 2 ResponseName: 'Response' ResponseTransform: 'none' NumObservations: 94 NumTrained: 100 Method: 'LSBoost' LearnerNames: {'Tree'} ReasonForTermination: [1x77 char] FitInfo: [100x1 double] FitInfoDescription: [2x83 char] Regularization: []
Predict the mileage of 4,000-pound cars with 4, 6, and 8 cylinders:
mileage4K = predict(ens,[4000 4; 4000 6; 4000 8]) mileage4K = 20.0294 19.4206 15.5000