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predict

Predict labels using discriminant analysis classification model

Syntax

label = predict(Mdl,X)
[label,score,cost] = predict(Mdl,X)

Description

label = predict(Mdl,X) returns a vector of predicted class labels for the predictor data in the table or matrix X, based on the trained discriminant analysis classification model Mdl.

[label,score,cost] = predict(Mdl,X) also returns:

  • A matrix of classification scores (score) indicating the likelihood that a label comes from a particular class. For discriminant analysis, scores are posterior probabilities.

  • A matrix of expected classification cost (cost). For each observation in X, the predicted class label corresponds to the minimum expected classification cost among all classes.

Input Arguments

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Discriminant analysis classification model, specified as a ClassificationDiscriminant or CompactClassificationDiscriminant model object returned by fitcdiscr.

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. All predictor variables in X must be numeric vectors.

  • For a numeric matrix, the variables that compose the columns of X must have the same order as the predictor variables that trained Mdl.

  • For a table:

    • predict does not support multi-column variables and cell arrays other than cell arrays of character vectors.

    • If you trained Mdl using a table (for example, Tbl), then all predictor variables in X must have the same variable names and data types as those that trained Mdl (stored in Mdl.PredictorNames). However, the column order of X does not need to correspond to the column order of Tbl. Tbl and X can contain additional variables (response variables, observation weights, etc.), but predict ignores them.

    • If you trained Mdl using a numeric matrix, then the predictor names in Mdl.PredictorNames and corresponding predictor variable names in X must be the same. To specify predictor names during training, see the PredictorNames name-value pair argument of fitcdiscr. X can contain additional variables (response variables, observation weights, etc.), but predict ignores them.

Data Types: table | double | single

Output Arguments

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Predicted class labels, returned as a categorical or character array, logical or numeric vector, or cell array of character vectors.

label:

  • Is the same data type as the observed class labels (Y) that trained Mdl. (The software treats string arrays as cell arrays of character vectors.)

  • Has length equal to the number of rows of X.

Predicted class posterior probabilities, returned as a numeric matrix of size N-by-K. N is the number of observations (rows) in X, and K is the number of classes (in Mdl.ClassNames). score(i,j) is the posterior probability that observation i in X is of class j in Mdl.ClassNames.

Expected classification costs, returned as a matrix of size N-by-K. N is the number of observations (rows) in X, and K is the number of classes (in Mdl.ClassNames). cost(i,j) is the cost of classifying row i of X as class j in Mdl.ClassNames.

Examples

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Load Fisher's iris data set. Determine the sample size.

load fisheriris
N = size(meas,1);

Partition the data into training and test sets. Hold out 10% of the data for testing.

rng(1); % For reproducibility
cvp = cvpartition(N,'Holdout',0.1);
idxTrn = training(cvp); % Training set indices
idxTest = test(cvp);    % Test set indices

Store the training data in a table.

tblTrn = array2table(meas(idxTrn,:));
tblTrn.Y = species(idxTrn);

Train a discriminant analysis model using the training set and default options.

Mdl = fitcdiscr(tblTrn,'Y');

Predict labels for the test set. You trained Mdl using a table of data, but you can predict labels using a matrix.

labels = predict(Mdl,meas(idxTest,:));

Construct a confusion matrix for the test set.

confusionchart(species(idxTest),labels);

Mdl misclassifies one versicolor iris as virginica in the test set.

Load Fisher's iris data set. Consider training using the petal lengths and widths only.

load fisheriris
X = meas(:,3:4);

Train a quadratic discriminant analysis model using the entire data set.

Mdl = fitcdiscr(X,species,'DiscrimType','quadratic');

Define a grid of values in the observed predictor space. Predict the posterior probabilities for each instance in the grid.

xMax = max(X);
xMin = min(X);
d = 0.01;
[x1Grid,x2Grid] = meshgrid(xMin(1):d:xMax(1),xMin(2):d:xMax(2));

[~,score] = predict(Mdl,[x1Grid(:),x2Grid(:)]);
Mdl.ClassNames
ans = 3x1 cell array
    {'setosa'    }
    {'versicolor'}
    {'virginica' }

score is a matrix of class posterior probabilities. The columns correspond to the classes in Mdl.ClassNames. For example, score(j,1) is the posterior probability that observation j is a setosa iris.

Plot the posterior probability of versicolor classification for each observation in the grid and plot the training data.

figure;
contourf(x1Grid,x2Grid,reshape(score(:,2),size(x1Grid,1),size(x1Grid,2)));
h = colorbar;
caxis([0 1]);
colormap jet;
hold on
gscatter(X(:,1),X(:,2),species,'mcy','.x+');
axis tight
title('Posterior Probability of versicolor'); 
hold off

The posterior probability region exposes a portion of the decision boundary.

More About

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Extended Capabilities

Introduced in R2011b