Documentation |
Classification ensemble grown by resampling
ClassificationBaggedEnsemble 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,'bag',nlearn,learners,'type','classification') creates a bagged classification ensemble. For syntax details, see the fitensemble 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 |
String describing how ens combines weak learner weights, either 'WeightedSum' or 'WeightedAverage'. |
FitInfo |
Numeric array of fit information. The FitInfoDescription property describes the content of this array. |
FitInfoDescription |
String describing the meaning of the FitInfo array. |
FResample |
Numeric scalar between 0 and 1. FResample is the fraction of training data fitensemble resampled at random for every weak learner when constructing the ensemble. |
Method |
String describing the method that creates ens. |
ModelParameters |
Parameters used in training ens. |
NumTrained |
Number of trained weak learners in ens, a scalar. |
PredictorNames |
Cell array of names for the predictor variables, in the order in which they appear in X. |
ReasonForTermination |
String describing the reason fitensemble stopped adding weak learners to the ensemble. |
Replace |
Logical value indicating if the ensemble was trained with replacement (true) or without replacement (false). |
ResponseName |
String with the name of the response variable Y. |
ScoreTransform |
Function handle for transforming scores, or string representing a built-in transformation function. 'none' means no transformation; equivalently, 'none' means @(x)x. For a list of built-in transformation functions and the syntax of custom transformation functions, see fitctree. Add or change a ScoreTransform function using dot notation: ens.ScoreTransform = 'function' or ens.ScoreTransform = @function |
Trained |
Trained learners, a cell array of compact classification models. |
TrainedWeights |
Numeric vector of trained weights for the weak learners in ens. TrainedWeights has T elements, where T is the number of weak learners in learners. |
UseObsForLearner |
Logical matrix of size N-by-NumTrained, where N is the number of observations in the training data and NumTrained is the number of trained weak learners. UseObsForLearner(I,J) is true if observation I was used for training learner J, and is false otherwise. |
W |
Scaled weights, a vector with length n, the number of rows in X. The sum of the elements of W is 1. |
X |
Matrix of predictor values that trained the ensemble. Each column of X represents one variable, and each row represents one observation. |
Y |
A categorical array, cell array of strings, character array, logical vector, or a numeric vector with the same number of rows as X. Each row of Y represents the classification of the corresponding row of X. |
oobEdge | Out-of-bag classification edge |
oobLoss | Out-of-bag classification error |
oobMargin | Out-of-bag classification margins |
oobPredict | Predict out-of-bag response of ensemble |
compact | Compact classification ensemble |
crossval | Cross validate ensemble |
resubEdge | Classification edge by resubstitution |
resubLoss | Classification error by resubstitution |
resubMargin | Classification margins by resubstitution |
resubPredict | Predict ensemble response by resubstitution |
resume | Resume training ensemble |
edge | Classification edge |
loss | Classification error |
margin | Classification margins |
predict | Predict classification |
predictorImportance | Estimates of predictor importance |
removeLearners | Remove members of compact classification ensemble |
Value. To learn how value classes affect copy operations, see Copying Objects in the MATLAB^{®} documentation.
Construct a bagged ensemble for the ionosphere data, and examine its resubstitution loss:
load ionosphere rng(0,'twister') % for reproducibility ens = fitensemble(X,Y,'bag',100,'Tree',... 'type','classification'); L = resubLoss(ens) L = 0
The ensemble does a perfect job classifying its training data.