Documentation |
ClassificationEnsemble 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. It also stores data used for training and can compute resubstitution predictions. It can resume training if desired.
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. |
ClassNames |
List of the elements in Y with duplicates removed. ClassNames can be a numeric vector, categorical vector, logical vector, character array, or cell array of strings. ClassNames has the same data type as the data in the argument Y. |
CombineWeights |
String describing how ens combines weak learner weights, either 'WeightedSum' or 'WeightedAverage'. |
Cost |
Square matrix where Cost(i,j) is the cost of classifying a point into class j if its true class is i. |
FitInfo |
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 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 |
String describing the method that creates ens. |
ModelParameters |
Parameters used in training ens. |
NumObservations |
Numeric scalar containing the number of observations in the training data. |
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. |
Prior |
Prior probabilities for each class. Prior is a numeric vector whose entries relate to the corresponding ClassNames property. |
ReasonForTermination |
String describing the reason fitensemble stopped adding weak learners to the ensemble. |
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. |
UsePredForLearner |
Logical matrix of size P-by-NumTrained, where P is the number of predictors (columns) in the training data X. UsePredForLearner(i,j) is true when learner j uses predictor i, and is false otherwise. For each learner, the predictors have the same order as the columns in the training data X. If the ensemble is not of type Subspace, all entries in UsePredForLearner are true. |
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 |
Numeric vector, categorical vector, logical vector, character array, or cell array of strings. Each row of Y represents the classification of the corresponding row of X. |
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 boosted classification ensemble for the ionosphere data, using the AdaBoostM1 method:
load ionosphere ens = fitensemble(X,Y,'AdaBoostM1',100,'Tree')
ens = classreg.learning.classif.ClassificationEnsemble PredictorNames: {1x34 cell} ResponseName: 'Y' ClassNames: {'b' 'g'} ScoreTransform: 'none' NumObservations: 351 NumTrained: 100 Method: 'AdaBoostM1' LearnerNames: {'Tree'} ReasonForTermination: 'Terminated normally after comp...' FitInfo: [100x1 double] FitInfoDescription: {2x1 cell} Properties, Methods
Predict the classification of the mean of X:
ypredict = predict(ens,mean(X))
ypredict = 'g'