resubPredict
Classify training data using trained classifier
Syntax
Description
[
specifies whether to include interaction terms in computations. This syntax applies only
to generalized additive models.label
,Score
] = resubPredict(Mdl
,'IncludeInteractions',includeInteractions
)
Examples
Label Training Sample Observations of Naive Bayes Classifier
Load the fisheriris
data set. Create X
as a numeric matrix that contains four measurements for 150 irises. Create Y
as a cell array of character vectors that contains the corresponding iris species.
load fisheriris X = meas; Y = species; rng('default') % For reproducibility
Train a naive Bayes classifier using the predictors X
and class labels Y
. A recommended practice is to specify the class names. fitcnb
assumes that each predictor is conditionally and normally distributed.
Mdl = fitcnb(X,Y,'ClassNames',{'setosa','versicolor','virginica'})
Mdl = ClassificationNaiveBayes ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'setosa' 'versicolor' 'virginica'} ScoreTransform: 'none' NumObservations: 150 DistributionNames: {'normal' 'normal' 'normal' 'normal'} DistributionParameters: {3x4 cell}
Mdl
is a trained ClassificationNaiveBayes
classifier.
Predict the training sample labels.
label = resubPredict(Mdl);
Display the results for a random set of 10 observations.
idx = randsample(size(X,1),10); table(Y(idx),label(idx),'VariableNames', ... {'True Label','Predicted Label'})
ans=10×2 table
True Label Predicted Label
______________ _______________
{'virginica' } {'virginica' }
{'setosa' } {'setosa' }
{'virginica' } {'virginica' }
{'versicolor'} {'versicolor'}
{'virginica' } {'virginica' }
{'versicolor'} {'versicolor'}
{'virginica' } {'virginica' }
{'setosa' } {'setosa' }
{'virginica' } {'virginica' }
{'setosa' } {'setosa' }
Create a confusion chart from the true labels Y
and the predicted labels label
.
cm = confusionchart(Y,label);
Estimate In-Sample Posterior Probabilities of SVM Classifier
Load the ionosphere
data set. This data set has 34 predictors and 351 binary responses for radar returns, either bad ('b'
) or good ('g'
).
load ionosphere
Train a support vector machine (SVM) classifier. Standardize the data and specify that 'g'
is the positive class.
SVMModel = fitcsvm(X,Y,'ClassNames',{'b','g'},'Standardize',true);
SVMModel
is a ClassificationSVM
classifier.
Fit the optimal score-to-posterior-probability transformation function.
rng(1); % For reproducibility
ScoreSVMModel = fitPosterior(SVMModel)
ScoreSVMModel = ClassificationSVM ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'b' 'g'} ScoreTransform: '@(S)sigmoid(S,-9.482430e-01,-1.217774e-01)' NumObservations: 351 Alpha: [90x1 double] Bias: -0.1342 KernelParameters: [1x1 struct] Mu: [0.8917 0 0.6413 0.0444 0.6011 0.1159 0.5501 0.1194 0.5118 0.1813 0.4762 0.1550 0.4008 0.0934 0.3442 0.0711 0.3819 -0.0036 0.3594 -0.0240 0.3367 0.0083 0.3625 -0.0574 0.3961 -0.0712 0.5416 -0.0695 0.3784 ... ] (1x34 double) Sigma: [0.3112 0 0.4977 0.4414 0.5199 0.4608 0.4927 0.5207 0.5071 0.4839 0.5635 0.4948 0.6222 0.4949 0.6528 0.4584 0.6180 0.4968 0.6263 0.5191 0.6098 0.5182 0.6038 0.5275 0.5785 0.5085 0.5162 0.5500 0.5759 0.5080 ... ] (1x34 double) BoxConstraints: [351x1 double] ConvergenceInfo: [1x1 struct] IsSupportVector: [351x1 logical] Solver: 'SMO'
Because the classes are inseparable, the score transformation function (ScoreSVMModel.ScoreTransform
) is the sigmoid function.
Estimate scores and positive class posterior probabilities for the training data. Display the results for the first 10 observations.
[label,scores] = resubPredict(SVMModel); [~,postProbs] = resubPredict(ScoreSVMModel); table(Y(1:10),label(1:10),scores(1:10,2),postProbs(1:10,2),'VariableNames',... {'TrueLabel','PredictedLabel','Score','PosteriorProbability'})
ans=10×4 table
TrueLabel PredictedLabel Score PosteriorProbability
_________ ______________ _______ ____________________
{'g'} {'g'} 1.4862 0.82216
{'b'} {'b'} -1.0003 0.30433
{'g'} {'g'} 1.8685 0.86917
{'b'} {'b'} -2.6457 0.084171
{'g'} {'g'} 1.2807 0.79186
{'b'} {'b'} -1.4616 0.22025
{'g'} {'g'} 2.1674 0.89816
{'b'} {'b'} -5.7085 0.00501
{'g'} {'g'} 2.4798 0.92224
{'b'} {'b'} -2.7812 0.074781
Compare GAMs by Examining Logit of Posterior Probabilities
Estimate the logit of posterior probabilities (classification scores) for training data using a classification generalized additive model (GAM) that contains both linear and interaction terms for predictors. Specify whether to include interaction terms when computing the classification scores.
Load the ionosphere
data set. This data set has 34 predictors and 351 binary responses for radar returns, either bad ('b'
) or good ('g'
).
load ionosphere
Train a GAM using the predictors X
and class labels Y
. A recommended practice is to specify the class names. Specify to include the 10 most important interaction terms.
Mdl = fitcgam(X,Y,'ClassNames',{'b','g'},'Interactions',10)
Mdl = ClassificationGAM ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'b' 'g'} ScoreTransform: 'logit' Intercept: 3.2565 Interactions: [10x2 double] NumObservations: 351
Mdl
is a ClassificationGAM
model object.
Predict the labels using both linear and interaction terms, and then using only linear terms. To exclude interaction terms, specify 'IncludeInteractions',false
. Estimate the logit of posterior probabilities by specifying the ScoreTransform
property as 'none'
.
Mdl.ScoreTransform = 'none'; [labels,scores] = resubPredict(Mdl); [labels_nointeraction,scores_nointeraction] = resubPredict(Mdl,'IncludeInteractions',false);
Create a table containing the true labels, predicted labels, and scores. Display the first eight rows of the table.
t = table(Y,labels,scores,labels_nointeraction,scores_nointeraction, ... 'VariableNames',{'True Labels','Predicted Labels','Scores' ... 'Predicted Labels Without Interactions','Scores Without Interactions'}); head(t)
True Labels Predicted Labels Scores Predicted Labels Without Interactions Scores Without Interactions ___________ ________________ __________________ _____________________________________ ___________________________ {'g'} {'g'} -51.628 51.628 {'g'} -47.676 47.676 {'b'} {'b'} 37.433 -37.433 {'b'} 36.435 -36.435 {'g'} {'g'} -62.061 62.061 {'g'} -58.357 58.357 {'b'} {'b'} 37.666 -37.666 {'b'} 36.297 -36.297 {'g'} {'g'} -47.361 47.361 {'g'} -43.373 43.373 {'b'} {'b'} 106.48 -106.48 {'b'} 102.43 -102.43 {'g'} {'g'} -62.665 62.665 {'g'} -58.377 58.377 {'b'} {'b'} 201.46 -201.46 {'b'} 197.84 -197.84
The predicted labels for the training data X
do not vary depending on the inclusion of interaction terms, but the estimated score values are different.
Estimate In-Sample Posterior Probabilities and Misclassification Costs of Naive Bayes Classifier
Estimate in-sample posterior probabilities and misclassification costs using a naive Bayes classifier.
Load the fisheriris
data set. Create X
as a numeric matrix that contains four measurements for 150 irises. Create Y
as a cell array of character vectors that contains the corresponding iris species.
load fisheriris X = meas; Y = species; rng('default') % For reproducibility
Train a naive Bayes classifier using the predictors X
and class labels Y
. A recommended practice is to specify the class names. fitcnb
assumes that each predictor is conditionally and normally distributed.
Mdl = fitcnb(X,Y,'ClassNames',{'setosa','versicolor','virginica'});
Mdl
is a trained ClassificationNaiveBayes
classifier.
Estimate the posterior probabilities and expected misclassification costs for the training data.
[label,Posterior,MisclassCost] = resubPredict(Mdl); Mdl.ClassNames
ans = 3x1 cell
{'setosa' }
{'versicolor'}
{'virginica' }
Display the results for 10 randomly selected observations.
idx = randsample(size(X,1),10); table(Y(idx),label(idx),Posterior(idx,:),MisclassCost(idx,:),'VariableNames', ... {'TrueLabel','PredictedLabel','PosteriorProbability','MisclassificationCost'})
ans=10×4 table
TrueLabel PredictedLabel PosteriorProbability MisclassificationCost
______________ ______________ _________________________________________ ______________________________________
{'virginica' } {'virginica' } 6.2514e-269 1.1709e-09 1 1 1 1.1709e-09
{'setosa' } {'setosa' } 1 5.5339e-19 2.485e-25 5.5339e-19 1 1
{'virginica' } {'virginica' } 7.4191e-249 1.4481e-10 1 1 1 1.4481e-10
{'versicolor'} {'versicolor'} 3.4472e-62 0.99997 3.362e-05 1 3.362e-05 0.99997
{'virginica' } {'virginica' } 3.4268e-229 6.597e-09 1 1 1 6.597e-09
{'versicolor'} {'versicolor'} 6.0941e-77 0.9998 0.00019663 1 0.00019663 0.9998
{'virginica' } {'virginica' } 1.3467e-167 0.002187 0.99781 1 0.99781 0.002187
{'setosa' } {'setosa' } 1 1.5776e-15 5.7172e-24 1.5776e-15 1 1
{'virginica' } {'virginica' } 2.0116e-232 2.6206e-10 1 1 1 2.6206e-10
{'setosa' } {'setosa' } 1 1.8085e-17 1.9639e-24 1.8085e-17 1 1
The order of the columns of Posterior
and MisclassCost
corresponds to the order of the classes in Mdl.ClassNames
.
Input Arguments
Mdl
— Classification machine learning model
full classification model object
Classification machine learning model, specified as a full classification model object, as given in the following table of supported models.
Model | Classification Model Object |
---|---|
Generalized additive model | ClassificationGAM |
k-nearest neighbor model | ClassificationKNN |
Naive Bayes model | ClassificationNaiveBayes |
Neural network model | ClassificationNeuralNetwork |
Support vector machine for one-class and binary classification | ClassificationSVM |
includeInteractions
— Flag to include interaction terms
true
| false
Flag to include interaction terms of the model, specified as true
or
false
. This argument is valid only for a generalized
additive model (GAM). That is, you can specify this argument only when
Mdl
is ClassificationGAM
.
The default value is true
if Mdl
contains interaction
terms. The value must be false
if the model does not contain interaction
terms.
Data Types: logical
Output Arguments
label
— Predicted class labels
categorical array | character array | logical vector | numeric vector | cell array of character vectors
Predicted class labels, returned as a categorical or character array, logical or numeric vector, or cell array of character vectors.
label
has the same data type as the observed class labels that
trained Mdl
, and its length is equal to the number of observations
in Mdl.X
. (The software treats string arrays as cell arrays of character
vectors.)
Score
— Class scores
numeric matrix
Class scores, returned as a numeric matrix. Score
has rows
equal to the number of observations in Mdl.X
and columns equal to the
number of distinct classes in the training data
(size(Mdl.ClassNames,1)
).
Cost
— Expected misclassification costs
numeric matrix
Expected misclassification costs, returned as a numeric matrix. This output applies
only to k-nearest neighbor and naive Bayes models. That is,
resubPredict
returns Cost
only when
Mdl
is ClassificationKNN
or
ClassificationNaiveBayes
.
Cost
has rows equal to the number of observations in
Mdl.X
and columns equal to the number of distinct classes in the
training data (size(Mdl.ClassNames,1)
).
Cost(j,k)
is the expected misclassification cost of the
observation in row j
of Mdl.X
predicted into class
k
(in class Mdl.ClassNames(k)
).
Algorithms
resubPredict
computes predictions according to the corresponding
predict
function of the object (Mdl
). For a
model-specific description, see the predict
function reference pages in
the following table.
Model | Classification Model Object (Mdl ) | predict Object Function |
---|---|---|
Generalized additive model | ClassificationGAM | predict |
k-nearest neighbor model | ClassificationKNN | predict |
Naive Bayes model | ClassificationNaiveBayes | predict |
Neural network model | ClassificationNeuralNetwork | predict |
Support vector machine for one-class and binary classification | ClassificationSVM | predict |
Extended Capabilities
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
Usage notes and limitations:
This function fully supports GPU arrays for a trained classification model specified as a
ClassificationKNN
,ClassificationNeuralNetwork
, orClassificationSVM
object.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2012aR2024b: Specify GPU arrays for neural network models (requires Parallel Computing Toolbox)
resubPredict
fully supports GPU arrays for ClassificationNeuralNetwork
.
R2023b: Observations with missing predictor values are used in resubstitution and cross-validation computations
Starting in R2023b, the following classification model object functions use observations with missing predictor values as part of resubstitution ("resub") and cross-validation ("kfold") computations for classification edges, losses, margins, and predictions.
In previous releases, the software omitted observations with missing predictor values from the resubstitution and cross-validation computations.
See Also
MATLAB 명령
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