predict
Predict labels for Gaussian kernel classification model
Description
Examples
Predict Training Set Labels
Predict the training set labels using a binary kernel classification model, and display the confusion matrix for the resulting classification.
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 binary kernel classification model that identifies whether the radar return is bad ('b'
) or good ('g'
).
rng('default') % For reproducibility Mdl = fitckernel(X,Y);
Mdl
is a ClassificationKernel
model.
Predict the training set, or resubstitution, labels.
label = predict(Mdl,X);
Construct a confusion matrix.
ConfusionTrain = confusionchart(Y,label);
The model misclassifies one radar return for each class.
Predict Test Set Labels
Predict the test set labels using a binary kernel classification model, and display the confusion matrix for the resulting classification.
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
Partition the data set into training and test sets. Specify a 15% holdout sample for the test set.
rng('default') % For reproducibility Partition = cvpartition(Y,'Holdout',0.15); trainingInds = training(Partition); % Indices for the training set testInds = test(Partition); % Indices for the test set
Train a binary kernel classification model using the training set. A good practice is to define the class order.
Mdl = fitckernel(X(trainingInds,:),Y(trainingInds),'ClassNames',{'b','g'});
Predict the training-set labels and the test set labels.
labelTrain = predict(Mdl,X(trainingInds,:)); labelTest = predict(Mdl,X(testInds,:));
Construct a confusion matrix for the training set.
ConfusionTrain = confusionchart(Y(trainingInds),labelTrain);
The model misclassifies only one radar return for each class.
Construct a confusion matrix for the test set.
ConfusionTest = confusionchart(Y(testInds),labelTest);
The model misclassifies one bad radar return as being a good return, and five good radar returns as being bad returns.
Estimate Posterior Class Probabilities
Estimate posterior class probabilities for a test set, and determine the quality of the model by plotting a receiver operating characteristic (ROC) curve. Kernel classification models return posterior probabilities for logistic regression learners only.
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
Partition the data set into training and test sets. Specify a 30% holdout sample for the test set.
rng('default') % For reproducibility Partition = cvpartition(Y,'Holdout',0.30); trainingInds = training(Partition); % Indices for the training set testInds = test(Partition); % Indices for the test set
Train a binary kernel classification model. Fit logistic regression learners.
Mdl = fitckernel(X(trainingInds,:),Y(trainingInds), ... 'ClassNames',{'b','g'},'Learner','logistic');
Predict the posterior class probabilities for the test set.
[~,posterior] = predict(Mdl,X(testInds,:));
Because Mdl
has one regularization strength, the output posterior
is a matrix with two columns and rows equal to the number of test-set observations. Column i
contains posterior probabilities of Mdl.ClassNames(i)
given a particular observation.
Compute the performance metrics (true positive rates and false positive rates) for a ROC curve and find the area under the ROC curve (AUC) value by creating a rocmetrics
object.
rocObj = rocmetrics(Y(testInds),posterior,Mdl.ClassNames);
Plot the ROC curve for the second class by using the plot
function of rocmetrics
.
plot(rocObj,ClassNames=Mdl.ClassNames(2))
The AUC is close to 1
, which indicates that the model predicts labels well.
Input Arguments
Mdl
— Binary kernel classification model
ClassificationKernel
model object
Binary kernel classification model, specified as a ClassificationKernel
model object. You can create a
ClassificationKernel
model object using fitckernel
.
X
— Predictor data to be classified
numeric matrix | table
Predictor data to be classified, specified as a numeric matrix or table.
Each row of X
corresponds to one observation, and
each column corresponds to one variable.
For a numeric matrix:
The variables in the columns of
X
must have the same order as the predictor variables that trainedMdl
.If you trained
Mdl
using a table (for example,Tbl
) andTbl
contains all numeric predictor variables, thenX
can be a numeric matrix. To treat numeric predictors inTbl
as categorical during training, identify categorical predictors by using theCategoricalPredictors
name-value pair argument offitckernel
. IfTbl
contains heterogeneous predictor variables (for example, numeric and categorical data types) andX
is a numeric matrix, thenpredict
throws an error.
For a table:
predict
does not support multicolumn variables or cell arrays other than cell arrays of character vectors.If you trained
Mdl
using a table (for example,Tbl
), then all predictor variables inX
must have the same variable names and data types as those that trainedMdl
(stored inMdl.PredictorNames
). However, the column order ofX
does not need to correspond to the column order ofTbl
. Also,Tbl
andX
can contain additional variables (response variables, observation weights, and so on), butpredict
ignores them.If you trained
Mdl
using a numeric matrix, then the predictor names inMdl.PredictorNames
and corresponding predictor variable names inX
must be the same. To specify predictor names during training, see thePredictorNames
name-value pair argument offitckernel
. All predictor variables inX
must be numeric vectors.X
can contain additional variables (response variables, observation weights, and so on), butpredict
ignores them.
Data Types: table
| double
| single
Output Arguments
Label
— Predicted class labels
categorical array | character array | logical matrix | numeric matrix | cell array of character vectors
Predicted class labels, returned as a categorical or character array, logical or numeric matrix, or cell array of character vectors.
Label
has n rows, where
n is the number of observations in
X
, and has the same data type as the observed class
labels (Y
) used to train Mdl
.
(The software treats string arrays as cell arrays of character
vectors.)
The predict
function classifies an observation into the class yielding the highest score. For an observation with NaN
scores, the
function classifies the observation into the majority class, which makes up the largest
proportion of the training labels.
Score
— Classification scores
numeric array
Classification scores, returned as an n-by-2
numeric array, where n is the number of observations in
X
.
Score(
is the score for classifying observation i
,j
)i
into
class j
. Mdl.ClassNames
stores
the order of the classes.
If Mdl.Learner
is 'logistic'
, then
classification scores are posterior probabilities.
More About
Classification Score
For kernel classification models, the raw classification score for classifying the observation x, a row vector, into the positive class is defined by
is a transformation of an observation for feature expansion.
β is the estimated column vector of coefficients.
b is the estimated scalar bias.
The raw classification score for classifying x into the negative class is −f(x). The software classifies observations into the class that yields a positive score.
If the kernel classification model consists of logistic regression learners, then the
software applies the 'logit'
score transformation to the raw
classification scores (see ScoreTransform
).
Extended Capabilities
Tall Arrays
Calculate with arrays that have more rows than fit in memory.
The
predict
function supports tall arrays with the following usage
notes and limitations:
predict
does not support talltable
data.
For more information, see Tall Arrays.
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™. (since R2023a)
Usage notes and limitations:
Use
saveLearnerForCoder
,loadLearnerForCoder
, andcodegen
(MATLAB Coder) to generate code for thepredict
function. Save a trained model by usingsaveLearnerForCoder
. Define an entry-point function that loads the saved model by usingloadLearnerForCoder
and calls thepredict
function. Then usecodegen
to generate code for the entry-point function.To generate single-precision C/C++ code for
predict
, specify the name-value argument"DataType","single"
when you call theloadLearnerForCoder
function.If the code generator uses the Open Multiprocessing (OpenMP) library, the generated code of
predict
splits the predictor dataX
into multiple chunks and predicts responses for the chunks in parallel. The generated code usesparfor
(MATLAB Coder) to create loops that run in parallel on supported shared-memory multicore platforms. If your compiler does not support the OpenMP application interface, or if you disable the OpenMP library, the generated code does not split the predictor data and, therefore, processes one observation at a time. To find supported compilers, see Supported Compilers. To disable the OpenMP library, set theEnableOpenMP
property of the configuration object tofalse
. For details, seecoder.CodeConfig
(MATLAB Coder).This table contains notes about the arguments of
predict
. Arguments not included in this table are fully supported.Argument Notes and Limitations Mdl
For the usage notes and limitations of the model object, see Code Generation of the
ClassificationKernel
object.X
For general code generation,
X
must be a single-precision or double-precision matrix or a table containing numeric variables, categorical variables, or both.The number of rows, or observations, in
X
can be a variable size, but the number of columns inX
must be fixed.If you want to specify
X
as a table, then your model must be trained using a table, and your entry-point function for prediction must do the following:Accept data as arrays.
Create a table from the data input arguments and specify the variable names in the table.
Pass the table to
predict
.
For an example of this table workflow, see Generate Code to Classify Data in Table. For more information on using tables in code generation, see Code Generation for Tables (MATLAB Coder) and Table Limitations for Code Generation (MATLAB Coder).
For more information, see Introduction to Code Generation.
Version History
Introduced in R2017bR2023a: Generate C/C++ code for prediction
You can generate C/C++ code for the predict
function.
See Also
ClassificationKernel
| fitckernel
| resume
| rocmetrics
| confusionchart
MATLAB 명령
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