CompactClassificationDiscriminant
Compact discriminant analysis classification
Description
A CompactClassificationDiscriminant
object
is a compact version of a discriminant analysis classifier. The compact version does not
include the data for training the classifier. Therefore, you cannot perform some tasks
with a compact classifier, such as cross validation. Use a compact classifier for making
predictions (classifications) of new data.
Creation
You can create a CompactClassificationDiscriminant
object in two ways:
Create a compact model from a full
ClassificationDiscriminant
model object by using thecompact
object function.Create a compact model by using the
makecdiscr
function and specifying the class meansMu
and covariance matrixSigma
.
Properties
BetweenSigma
— Between-class covariance
square matrix
This property is read-only.
Between-class covariance, specified as a p
-by-p
matrix, where p
is the number of predictors.
Data Types: double
CategoricalPredictors
— Categorical predictor indices
[]
This property is read-only.
Categorical predictor indices, which is always empty ([]
).
ClassNames
— Class names in training data Y
categorical array | cell array of character vectors | character array | string array | logical vector | numeric vector
This property is read-only.
Class names in the training data Y
with duplicates removed.
ClassNames
has the same data type as the data in the argument
Y
in the training data. ClassNames
can have
the following data types:
Categorical array
Cell array of character vectors
Character array
Logical vector
Numeric vector
(The software treats string arrays as cell arrays of character vectors.)
Data Types: single
| double
| logical
| char
| string
| cell
| categorical
Coeffs
— Coefficient matrices
k
-by-k
structure | []
This property is read-only.
Coefficient matrices, specified as a k
-by-k
structure, where k
is the number of classes. If fitcdiscr
had the FillCoeffs
name-value pair set to
'off'
when constructing the classifier, Coeffs
is empty ([]
).
Coeffs(i,j)
contains coefficients of the linear or quadratic
boundaries between classes i
and j
. Fields in
Coeffs(i,j)
:
DiscrimType
Class1
—ClassNames
(i)
Class2
—ClassNames
(j)
Const
— A scalarLinear
— A vector withp
components, wherep
is the number of columns inX
Quadratic
—p
-by-p
matrix, exists for quadraticDiscrimType
The equation of the boundary between class i
and class
j
is
Const
+ Linear
* x
+ x'
* Quadratic
* x
=
0
,
where x
is a column vector of length p
.
Data Types: struct
Cost
— Cost of classifying a point
square matrix
Cost of classifying a point, specified as a square matrix.
Cost(i,j)
is the cost of classifying a point into class
j
if its true class is i
(the rows correspond
to the true class and the columns correspond to the predicted class). The order of the
rows and columns of Cost
corresponds to the order of the classes in
ClassNames
. The number of rows and columns in
Cost
is the number of unique classes in the response.
Change a Cost
matrix using dot notation: obj.Cost =
costMatrix
.
Data Types: double
Delta
— Delta threshold for a linear discriminant model
nonnegative scalar
Value of the Delta threshold for a linear discriminant model, specified as a
nonnegative scalar. If a coefficient of obj
has magnitude smaller
than Delta
, obj
sets this coefficient to
0
, and so you can eliminate the corresponding predictor from the
model. Set Delta
to a higher value to eliminate more
predictors.
Delta
must be 0
for quadratic discriminant
models.
Change Delta
using dot notation: obj.Delta =
newDelta
.
Data Types: double
DeltaPredictor
— Minimum value of Delta coefficient for predictor to be in model
row vector of length p
This property is read-only.
Minimum value of Delta coefficient for predictor to be in model, specified as a row
vector of length p
, where p
is the number of
predictors in obj
. If
DeltaPredictor(i) < Delta
then coefficient
i
of the model is 0
.
If obj
is a quadratic discriminant model, all elements of
DeltaPredictor
are 0
.
Data Types: double
DiscrimType
— Discriminant type
character vector
Discriminant type, specified as a character vector or string. Available values:
'linear'
'quadratic'
'diagLinear'
'diagQuadratic'
'pseudoLinear'
'pseudoQuadratic'
Change DiscrimType
using dot notation: obj.DiscrimType =
newDiscrimType
. You can change between linear types, or between quadratic
types, but cannot change between linear and quadratic types.
Data Types: char
| string
Gamma
— Gamma regularization parameter
scalar from 0
through 1
Value of the Gamma regularization parameter, specified as a scalar from
0
through 1
. Change Gamma
using dot notation: obj.Gamma = newGamma
.
If you set
1
for linear discriminant, the discriminant sets its type to'diagLinear'
.If you set a value between
MinGamma
and1
for linear discriminant, the discriminant sets its type to'linear'
.You cannot set values below the value of the
MinGamma
property.For quadratic discriminant, you can set either
0
(forDiscrimType
'quadratic'
) or1
(forDiscrimType
'diagQuadratic'
).
Data Types: double
LogDetSigma
— Logarithm of determinant of within-class covariance matrix
scalar | vector
This property is read-only.
Logarithm of the determinant of the within-class covariance matrix, returned as a
scalar or vector. The type of LogDetSigma
depends on the discriminant
type:
Scalar for linear discriminant analysis
Vector of length
K
for quadratic discriminant analysis, whereK
is the number of classes
Data Types: double
MinGamma
— Minimal value of Gamma parameter so that correlation matrix is invertible
nonnegative scalar
This property is read-only.
Minimal value of the Gamma parameter so that the correlation matrix is invertible,
returned as a nonnegative scalar. If the correlation matrix is not singular,
MinGamma
is 0
.
Mu
— Class means
real K
-by-p
matrix
This property is read-only.
Class means, specified as a K
-by-p
matrix of
real values. K
is the number of classes, and p
is
the number of predictors. Each row of Mu
represents the mean of the
multivariate normal distribution of the corresponding class. The class indices are in
the ClassNames
attribute.
Data Types: double
PredictorNames
— Names of predictor variables
cell array
This property is read-only.
Names of predictor variables, returned as a cell array. The names are in the order in
which they appear in the training data X
.
Data Types: cell
Prior
— Prior probabilities for each class
numeric vector
Prior probabilities for each class, returned as a numeric vector. The order of the
elements of Prior
corresponds to the order of the classes in
ClassNames
.
Add or change a Prior
vector using dot notation: obj.Prior
= priorVector
.
Data Types: double
ResponseName
— Name of the response variable Y
character vector
This property is read-only.
Name of the response variable Y
, returned as a character
vector.
Data Types: char
| string
ScoreTransform
— Score transformation function
name of a built-in function | function handle | 'none'
Score transformation function, specified as a character vector or string representing
a built-in transformation function, or as a function handle for transforming scores.
'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
fitcdiscr
.
Implement dot notation to add or change a ScoreTransform
function
using one of the following:
cobj.ScoreTransform = '
function
'cobj.ScoreTransform = @
function
Data Types: char
| string
| function_handle
Sigma
— Within-class covariance
numeric array
This property is read-only.
Within-class covariance, returned as a numeric array. The dimensions depend on
DiscrimType
:
'linear'
(default) — Matrix of sizep
-by-p
, wherep
is the number of predictors'quadratic'
— Array of sizep
-by-p
-by-K
, whereK
is the number of classes'diagLinear'
— Row vector of lengthp
'diagQuadratic'
— Array of size1
-by-p
-by-K
'pseudoLinear'
— Matrix of sizep
-by-p
'pseudoQuadratic'
— Array of sizep
-by-p
-by-K
Data Types: double
Object Functions
compareHoldout | Compare accuracies of two classification models using new data |
edge | Classification edge for discriminant analysis classifier |
lime | Local interpretable model-agnostic explanations (LIME) |
logp | Log unconditional probability density for discriminant analysis classifier |
loss | Classification loss for discriminant analysis classifier |
mahal | Mahalanobis distance to class means of discriminant analysis classifier |
margin | Classification margins for discriminant analysis classifier |
nLinearCoeffs | Number of nonzero linear coefficients in discriminant analysis classifier |
partialDependence | Compute partial dependence |
plotPartialDependence | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |
predict | Predict labels using discriminant analysis classifier |
shapley | Shapley values |
Examples
Construct a Compact Discriminant Analysis Classifier
Load the sample data.
load fisheriris
Construct a discriminant analysis classifier for the sample data.
fullobj = fitcdiscr(meas,species);
Construct a compact discriminant analysis classifier, and compare its size to that of the full classifier.
cobj = compact(fullobj); b = whos('fullobj'); % b.bytes = size of fullobj c = whos('cobj'); % c.bytes = size of cobj [b.bytes c.bytes] % shows cobj uses 60% of the memory
ans = 1×2
19139 12328
The compact classifier is smaller than the full classifier.
Construct Classifier Using Means and Covariances
Construct a compact discriminant analysis classifier from the means and covariances of the Fisher iris data.
load fisheriris mu(1,:) = mean(meas(1:50,:)); mu(2,:) = mean(meas(51:100,:)); mu(3,:) = mean(meas(101:150,:)); mm1 = repmat(mu(1,:),50,1); mm2 = repmat(mu(2,:),50,1); mm3 = repmat(mu(3,:),50,1); cc = meas; cc(1:50,:) = cc(1:50,:) - mm1; cc(51:100,:) = cc(51:100,:) - mm2; cc(101:150,:) = cc(101:150,:) - mm3; sigstar = cc' * cc / 147; cpct = makecdiscr(mu,sigstar,... 'ClassNames',{'setosa','versicolor','virginica'});
More About
Discriminant Classification
The model for discriminant analysis is:
Each class (
Y
) generates data (X
) using a multivariate normal distribution. That is, the model assumesX
has a Gaussian mixture distribution (gmdistribution
).For linear discriminant analysis, the model has the same covariance matrix for each class, only the means vary.
For quadratic discriminant analysis, both means and covariances of each class vary.
predict
classifies so as to minimize the expected
classification cost:
where
is the predicted classification.
K is the number of classes.
is the posterior probability of class k for observation x.
is the cost of classifying an observation as y when its true class is k.
For details, see Prediction Using Discriminant Analysis Models.
Regularization
Regularization is the process of finding a small set of predictors
that yield an effective predictive model. For linear discriminant
analysis, there are two parameters, γ and δ,
that control regularization as follows. cvshrink
helps
you select appropriate values of the parameters.
Let Σ represent the covariance matrix of the data X, and let be the centered data (the data X minus the mean by class). Define
The regularized covariance matrix is
Whenever γ ≥ MinGamma
, is nonsingular.
Let μk be the mean vector for those elements of X in class k, and let μ0 be the global mean vector (the mean of the rows of X). Let C be the correlation matrix of the data X, and let be the regularized correlation matrix:
where I is the identity matrix.
The linear term in the regularized discriminant analysis classifier for a data point x is
The parameter δ enters into this equation as a threshold on the final term in square brackets. Each component of the vector is set to zero if it is smaller in magnitude than the threshold δ. Therefore, for class k, if component j is thresholded to zero, component j of x does not enter into the evaluation of the posterior probability.
The DeltaPredictor
property is a vector related
to this threshold. When δ ≥ DeltaPredictor(i)
, all classes k have
Therefore, when δ ≥ DeltaPredictor(i)
, the regularized
classifier does not use predictor i
.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
The
predict
function supports code generation.When you train a discriminant analysis model by using
fitcdiscr
or create a compact discriminant analysis model by usingmakecdiscr
, the value of the'ScoreTransform'
name-value pair argument cannot be an anonymous function.
For more information, see Introduction to Code Generation.
Version History
Introduced in R2011b
See Also
ClassificationDiscriminant
| compact
| makecdiscr
| fitcdiscr
| predict
| compareHoldout
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