margin

Classification margins for classification ensemble model

Syntax

m = margin(ens,tbl,ResponseVarName)

m = margin(ens,tbl,Y)

m = margin(ens,X,Y)

m = margin(___,Name=Value)

Description

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

The classification margin is the difference between the classification score for the true class and the maximal classification score for the false classes. m is a column vector with the same number of rows as in tbl.

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

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

m = margin(___,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, and perform computations in parallel.

Examples

Find Classification Margin

Find the margin for classifying an average flower from the fisheriris data as 'versicolor'.

Load the Fisher iris data set.

load fisheriris

Train an ensemble of 100 boosted classification trees using AdaBoostM2.

t = templateTree(MaxNumSplits=1); % Weak learner template tree object
ens = fitcensemble(meas,species,"Method","AdaBoostM2","Learners",t);

Classify an average flower and find the classification margin.

flower = mean(meas);
predict(ens,flower)

ans = 1×1 cell array
    {'versicolor'}

margin(ens,flower,"versicolor")

ans = 
3.2140

Input Arguments

ens — Classification ensemble model

ClassificationEnsemble model object | ClassificationBaggedEnsemble model object | CompactClassificationEnsemble model object

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

tbl — Sample data

table

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 margin must also be in a table.

Data Types: table

ResponseVarName — Response variable name

name of variable in tbl

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

Y — Class labels

categorical array | character array | string array | logical vector | numeric vector | cell array of character vectors

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

X — Predictor data

numeric matrix

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 margin 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: margin(Mdl,X,Learners=[1 2],UseParallel=true) specifies to use the first two learners in the ensemble to calculate margins, and to run in parallel.

Learners — Indices of weak learners

[1:ens.NumTrained] (default) | vector of positive integers

Indices of the weak learners in the ensemble to use with margin, specified as a vector of positive integers in the range [1:ens.NumTrained]. By default, the function uses all learners.

Example: Learners=[1 2 4]

Data Types: single | double

UseObsForLearner — Option to use observations for learners

true(N,T) (default) | logical matrix

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

UseParallel — Flag to run in parallel

false or 0 (default) | true or 1

Flag to run in parallel, specified as a numeric or logical 1 (true) or 0 (false). If you specify UseParallel=true, the margin 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

More About

Classification Margin

The classification margin is the difference between the classification score for the true class and maximal classification score for the false classes. Margin is a column vector with the same number of rows as in the matrix X.

Score (ensemble)

For ensembles, a classification score represents the confidence of a classification into a class. The higher the score, the higher the confidence.

Different ensemble algorithms have different definitions for their scores. Furthermore, the range of scores depends on ensemble type. For example:

• AdaBoostM1 scores range from –∞ to ∞.

• Bag scores range from 0 to 1.

Extended Capabilities

Tall Arrays

Calculate with arrays that have more rows than fit in memory.

Usage notes and limitations:

• You cannot use the UseParallel name-value argument with tall arrays.

For more information, see Tall Arrays.

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run in parallel, set the UseParallel name-value argument to true in the call to this function.

For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).

You cannot use the UseParallel name-value argument with tall arrays, GPU arrays, or code generation.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• The margin function does not support ensembles trained using decision tree learners with surrogate splits.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2011a