CompactClassificationDiscriminant
A CompactClassificationDiscriminant object is a compact version of a
discriminant analysis model, ClassificationDiscriminant.
The CompactClassificationDiscriminant does not include the training
data resulting to a smaller classifier size, which can be used for making
predictions from new data, but not for tasks such as cross validation. It
can only be created from a ClassificationDiscriminant model by using
the compact object method.
The available methods for a CompactClassificationDiscriminant object
are:
predict
loss
margin
savemodel
See also: fitcdiscr, compact, ClassificationDiscriminant
Source Code: CompactClassificationDiscriminant
loss
Compute loss for a trained CompactClassificationDiscriminant object.
L = loss (obj, X, Y) computes the loss,
L, using the default loss function 'mincost'.
obj is a CompactClassificationDiscriminant object trained on
X and Y.
X must be a numeric matrix of input data where rows
correspond to observations and columns correspond to features or
variables.
Y is matrix or cell matrix containing the class labels
of corresponding predictor data in X. Y must have same
numbers of Rows as X.
L = loss (…, name, value) allows
additional options specified by name-value pairs:
| Name | Value | |
|---|---|---|
"LossFun" | Specifies the loss function to use.
Can be a function handle with four input arguments (C, S, W, Cost)
which returns a scalar value or one of:
’binodeviance’, ’classifcost’, ’classiferror’, ’exponential’,
’hinge’, ’logit’,’mincost’, ’quadratic’.
| |
"Weights" | Specifies observation weights, must be
a numeric vector of length equal to the number of rows in X.
Default is ones (size (X, 1)). loss normalizes the weights so that
observation weights in each class sum to the prior probability of that
class. When you supply Weights, loss computes the weighted
classification loss. |
See also: CompactClassificationDiscriminant
margin
m = margin (obj, X, Y) returns
the classification margins for obj with data X and
classification Y. m is a numeric vector of length size (X,1).
obj is a CompactClassificationDiscriminant object trained on X
and Y.
X must be a numeric matrix of input data where rows
correspond to observations and columns correspond to features or
variables.
Y is matrix or cell matrix containing the class labels
of corresponding predictor data in X. Y must have same
numbers of Rows as X.
The classification margin for each observation is the difference between the classification score for the true class and the maximal classification score for the false classes.
See also: fitcdiscr, CompactClassificationDiscriminant
predict
Classify new data points into categories using the discriminant analysis model from a CompactClassificationDiscriminant object.
label = predict (obj, XC) returns the vector of
labels predicted for the corresponding instances in XC, using the
corresponding labels from the trained ClassificationDiscriminant,
model, obj.
CompactClassificationDiscriminant class object.
[label, score, cost] = predict (obj,
XC) also returns score, which contains the predicted class
scores or posterior probabilities for each instance of the corresponding
unique classes, and cost, which is a matrix containing the expected
cost of the classifications.
The score matrix contains the posterior probabilities for each class, calculated using the multivariate normal probability density function and the prior probabilities of each class. These scores are normalized to ensure they sum to 1 for each observation.
The cost matrix contains the expected classification cost for each class, computed based on the posterior probabilities and the specified misclassification costs.
See also: CompactClassificationDiscriminant, fitcdiscr
savemodel
Save a CompactClassificationDiscriminant object.
savemodel (obj, filename) saves a
CompactClassificationDiscriminant object into a file defined by filename.
See also: loadmodel, fitcdiscr, CompactClassificationDiscriminant
## Create a discriminant analysis classifier and its compact version
# and compare their size
load fisheriris
X = meas;
Y = species;
Mdl = fitcdiscr (X, Y, 'ClassNames', unique (species))
CMdl = crossval (Mdl);
whos ('Mdl', 'CMdl')
Mdl =
ClassificationDiscriminant object with properties:
ClassNames: [3x1 cell]
Coeffs: [3x3 struct]
Cost: [3x3 double]
Delta: [1x1 double]
DiscrimType: linear
Gamma: [1x1 double]
LogDetSigma: [1x1 double]
MinGamma: [1x1 double]
Mu: [3x4 double]
NumObservations: [1x1 double]
NumPredictors: [1x1 double]
PredictorNames: [1x4 cell]
Prior: [3x1 double]
ResponseName: Y
RowsUsed: [150x1 double]
ScoreTransform: none
Sigma: [4x4 double]
X: [150x4 double]
XCentered: [150x4 double]
Y: [150x1 cell]
Variables visible from the current scope:
variables in scope: build_DEMOS: /home/promitheas/.local/share/octave/api-v59/packages/pkg-octave-doc-0.5.5/build_DEMOS.m
Attr Name Size Bytes Class
==== ==== ==== ===== =====
Mdl 1x1 0 ClassificationDiscriminant
CMdl 1x1 0 ClassificationPartitionedModel
Total is 2 elements using 0 bytes
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