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margin

Class: CompactClassificationTree

Classification margins

Syntax

m = margin(tree,TBL,ResponseVarName)
m = margin(tree,TBL,Y)
m = margin(tree,X,Y)

Description

m = margin(tree,TBL,ResponseVarName) returns the classification margins for the table of predictors TBL and class labels TBL.ResponseVarName. For the definition, see Margin.

m = margin(tree,TBL,Y) returns the classification margins for the table of predictors TBL and class labels Y.

m = margin(tree,X,Y) returns the classification margins for the matrix of predictors X and class labels Y.

Input Arguments

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Trained classification tree, specified as a ClassificationTree or CompactClassificationTree model object. That is, tree is a trained classification model returned by fitctree or compact.

Sample data, specified as a table. Each row of TBL corresponds to one observation, and each column corresponds to one predictor variable. Optionally, TBL can contain additional columns for the response variable and observation weights. TBL must contain all the predictors used to train tree. Multi-column variables and cell arrays other than cell arrays of character vectors are not allowed.

If TBL contains the response variable used to train tree, then you do not need to specify ResponseVarName or Y.

If you train tree using sample data contained in a table, then the input data for this method must also be in a table.

Data Types: table

Data to classify, specified as a numeric matrix. 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 tree. X must have the same number of rows as the number of elements in Y.

Data Types: single | double

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

If you specify ResponseVarName, then you must do so as a character vector. For example, if the response variable is stored as TBL.Response, then specify it as 'Response'. Otherwise, the software treats all columns of TBL, including TBL.ResponseVarName, as predictors.

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

Class labels, specified as a categorical or character array, a logical or numeric vector, or a cell array of character vectors. Y must be of the same type as the classification used to train tree, and its number of elements must equal the number of rows of X.

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

Output Arguments

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Margin, returned as a numeric column vector of length size(X,1). Each entry in m represents the margin for the corresponding rows of X and (true class) Y, computed using tree.

Examples

Compute the classification margin for the Fisher iris data, trained on its first two columns of data, and view the last 10 entries.

load fisheriris
X = meas(:,1:2);
tree = fitctree(X,species);
M = margin(tree,X,species);
M(end-10:end)
ans =
    0.1111
    0.1111
    0.1111
   -0.2857
    0.6364
    0.6364
    0.1111
    0.7500
    1.0000
    0.6364
    0.2000

The classification tree trained on all the data is better.

tree = fitctree(meas,species);
M = margin(tree,meas,species);
M(end-10:end)
ans =
    0.9565
    0.9565
    0.9565
    0.9565
    0.9565
    0.9565
    0.9565
    0.9565
    0.9565
    0.9565
    0.9565

Definitions

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Extended Capabilities

See Also

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