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edge

Classification edge for classification ensemble model

Description

e = edge(ens,tbl,ResponseVarName) returns the classification edge e for the trained classification ensemble model ens using the predictor data in table tbl and the true class labels in tbl.ResponseVarName.

The classification edge e is a vector or scalar depending on the setting of the Mode name-value argument.

e = edge(ens,tbl,Y) returns the classification edge using the predictor data in table tbl and the true class labels in Y.

example

e = edge(ens,X,Y) returns the classification edge using the predictor data in matrix X and the true class labels in Y.

e = edge(___,Name=Value) specifies options using one or more name-value arguments in addition to any of the input argument combinations in the previous syntaxes. For example, you can specify the indices of weak learners in the ensemble to use for calculating margins, specify observation weights, and perform computations in parallel.

Note

If the predictor data X or the predictor variables in tbl contain any missing values, the edge function might return NaN. For more details, see edge might return NaN for predictor data with missing values.

Input Arguments

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Full classification ensemble model, specified as a ClassificationEnsemble model object trained with fitcensemble, or a CompactClassificationEnsemble model object created with compact.

Sample data, specified as a table. Each row of tbl corresponds to one observation, and each column corresponds to one predictor variable. tbl must contain all of the predictors used to train the model. Multicolumn variables and cell arrays other than cell arrays of character vectors are not allowed.

If you trained ens using sample data contained in a table, then the input data for edge must also be in a table.

Data Types: table

Response variable name, specified as the name of a variable in tbl. If tbl contains the response variable used to train ens, then you do not need to specify ResponseVarName.

If you specify ResponseVarName, you must specify it as a character vector or string scalar. For example, if the response variable Y is stored as tbl.Y, then specify it as "Y". Otherwise, the software treats all columns of tbl, including Y, as predictors.

The response variable must be a categorical, character, or string array, a logical or numeric vector, or a cell array of character vectors. If the response variable is a character array, then each element must correspond to one row of the array.

Data Types: char | string

Class labels, specified as a categorical, character, or string array, a logical or numeric vector, or a cell array of character vectors. Y must have the same data type as tbl or X. (The software treats string arrays as cell arrays of character vectors.)

Y must be of the same type as the classification used to train ens, and its number of elements must equal the number of rows of tbl or X.

Data Types: categorical | char | string | logical | single | double | cell

Predictor data, specified as a numeric matrix.

Each row of X corresponds to one observation, and each column corresponds to one variable. The variables in the columns of X must be the same as the variables used to train ens.

The number of rows in X must equal the number of rows in Y.

If you trained ens using sample data contained in a matrix, then the input data for edge must also be in a matrix.

Data Types: double | single

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: edge(Mdl,X,Mode="individual",UseParallel=true) specifies to output a vector with one element per trained learner, and to run in parallel.

Indices of weak learners in the ensemble to use in edge, specified as a vector of positive integers in the range [1:ens.NumTrained]. By default, all learners are used.

Example: Learners=[1 2 4]

Data Types: single | double

Aggregation level for the output, specified as "ensemble", "individual", or "cumulative".

ValueDescription
"ensemble"The output is a scalar value, the loss for the entire ensemble.
"individual"The output is a vector with one element per trained learner.
"cumulative"The output is a vector in which element J is obtained by using learners 1:J from the input list of learners.

Example: Mode="individual"

Data Types: char | string

Option to use observations for learners, specified as a logical matrix of size N-by-T, where:

  • N is the number of rows of X.

  • T is the number of weak learners in ens.

When UseObsForLearner(i,j) is true (default), learner j is used in predicting the class of row i of X.

Example: UseObsForLearner=logical([1 1; 0 1; 1 0])

Data Types: logical matrix

Flag to run in parallel, specified as a numeric or logical 1 (true) or 0 (false). If you specify UseParallel=true, the edge function executes for-loop iterations by using parfor. The loop runs in parallel when you have Parallel Computing Toolbox™.

Example: UseParallel=true

Data Types: logical

Observation weights, specified as a numeric vector or the name of a variable in tbl. If you supply weights, edge computes the weighted classification edge.

If you specify Weights as a numeric vector, then the size of Weights must be equal to the number of observations in X or tbl. The software normalizes Weights to sum up to the value of the prior probability in the respective class.

If you specify Weights as the name of a variable in tbl, you must specify it as a character vector or string scalar. For example, if the weights are stored as tbl.w, then specify Weights as "w". Otherwise, the software treats all columns of tbl, including tbl.w, as predictors.

Data Types: single | double | char | string

Examples

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Find the classification edge for some of the data used to train a boosted ensemble classifier.

Load the ionosphere data set.

load ionosphere

Train an ensemble of 100 boosted classification trees using AdaBoostM1.

t = templateTree(MaxNumSplits=1); % Weak learner template tree object
ens = fitcensemble(X,Y,"Method","AdaBoostM1","Learners",t);

Find the classification edge for the last few rows.

E = edge(ens,X(end-10:end,:),Y(end-10:end))
E = 8.3310

More About

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Extended Capabilities

Version History

Introduced in R2011a

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