margin

Classification margins

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 margin for the predictions of ens on data tbl, when the true classifications are tbl.ResponseVarName.

M = margin(ens,tbl,Y) returns the classification margin for the predictions of ens on data tbl, when the true classifications are Y.

M = margin(ens,X,Y) returns the classification margin for the predictions of ens on data X, when the true classifications are Y.

M = margin(___Name,Value) calculates margin with additional options specified by one or more Name,Value pair arguments, using any of the previous syntaxes.

Input Arguments

ens	Classification ensemble created with fitcensemble, or a compact classification ensemble created with compact.
tbl	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 this method must also be in a table.
ResponseVarName	Response variable name, specified as the name of a variable in tbl. You must specify ResponseVarName 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 when training the model.
X	Matrix of data to classify. Each row of X represents one observation, and each column represents one predictor. X must have the same number of columns as the data used to train ens. X should have the same number of rows as the number of elements in Y. If you trained ens using sample data contained in a matrix, then the input data for this method must also be in a matrix.
Y	Class labels of observations in tbl or X. Y should be of the same type as the classification used to train ens, and its number of elements should equal the number of rows of tbl or X.

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.

learners	Indices of weak learners in the ensemble ranging from 1 to ens.NumTrained. margin uses only these learners for calculating loss. Default: 1:NumTrained
UseObsForLearner	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, learner j is used in predicting the class of row i of X. Default: true(N,T)
UseParallel	Indication to perform inference in parallel, specified as false (compute serially) or true (compute in parallel). Parallel computation requires Parallel Computing Toolbox™. Parallel inference can be faster than serial inference, especially for large datasets. Parallel computation is supported only for tree learners. Default: false

Output Arguments

M	A numeric column vector with the same number of rows as tbl or X. Each row of M gives the classification margin for that row of tbl or X.

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 = 1x1 cell array
    {'versicolor'}

margin(ens,flower,'versicolor')

ans = 3.2140

More About

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 UseParallel 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 UseParallel with tall or GPU arrays.

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).