predict - Classify observations using naive Bayes classifier - MATLAB (original) (raw)

Classify observations using naive Bayes classifier

Syntax

Description

[label](#budv6du-1-label) = predict([Mdl](#budvxvy-1%5Fsep%5Fshared-Mdl),[X](#budvxvy-1-X)) returns a vector of predicted class labels for the predictor data in the table or matrix X, based on the trained naive Bayes classification modelMdl. The trained naive Bayes model can either be full or compact.

example

[[label](#budv6du-1-label),[Posterior](#mw%5F4035643c-21a8-464a-a094-a33d73203ea4),[Cost](#budv6du-1-Cost)] = predict([Mdl](#budvxvy-1%5Fsep%5Fshared-Mdl),[X](#budvxvy-1-X)) also returns thePosterior Probability (Posterior) and predicted (expected) Misclassification Cost (Cost) corresponding to the observations (rows) in Mdl.X. For each observation inX, the predicted class label corresponds to the minimum expected classification cost among all classes.

example

Examples

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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

Randomly partition observations into a training set and a test set with stratification, using the class information in Y. Specify a 30% holdout sample for testing.

cv = cvpartition(Y,'HoldOut',0.30);

Extract the training and test indices.

trainInds = training(cv); testInds = test(cv);

Specify the training and test data sets.

XTrain = X(trainInds,:); YTrain = Y(trainInds); XTest = X(testInds,:); YTest = Y(testInds);

Train a naive Bayes classifier using the predictors XTrain and class labels YTrain. A recommended practice is to specify the class names. fitcnb assumes that each predictor is conditionally and normally distributed.

Mdl = fitcnb(XTrain,YTrain,'ClassNames',{'setosa','versicolor','virginica'})

Mdl = ClassificationNaiveBayes ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'setosa' 'versicolor' 'virginica'} ScoreTransform: 'none' NumObservations: 105 DistributionNames: {'normal' 'normal' 'normal' 'normal'} DistributionParameters: {3×4 cell}

Properties, Methods

Mdl is a trained ClassificationNaiveBayes classifier.

Predict the test sample labels.

idx = randsample(sum(testInds),10); label = predict(Mdl,XTest);

Display the results for a random set of 10 observations in the test sample.

table(YTest(idx),label(idx),'VariableNames',... {'TrueLabel','PredictedLabel'})

ans=10×2 table TrueLabel PredictedLabel ______________ ______________

{'virginica' }    {'virginica' }
{'versicolor'}    {'versicolor'}
{'versicolor'}    {'versicolor'}
{'virginica' }    {'virginica' }
{'setosa'    }    {'setosa'    }
{'virginica' }    {'virginica' }
{'setosa'    }    {'setosa'    }
{'versicolor'}    {'versicolor'}
{'versicolor'}    {'virginica' }
{'versicolor'}    {'versicolor'}

Create a confusion chart from the true labels YTest and the predicted labels label.

cm = confusionchart(YTest,label);

Estimate posterior probabilities and misclassification costs for new observations using a naive Bayes classifier. Classify new observations using a memory-efficient pretrained 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

Partition the data set into two sets: one contains the training set, and the other contains new, unobserved data. Reserve 10 observations for the new data set.

n = size(X,1); newInds = randsample(n,10); inds = ~ismember(1:n,newInds); XNew = X(newInds,:); YNew = Y(newInds);

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(inds,:),Y(inds),... 'ClassNames',{'setosa','versicolor','virginica'});

Mdl is a trained ClassificationNaiveBayes classifier.

Conserve memory by reducing the size of the trained naive Bayes classifier.

CMdl = compact(Mdl); whos('Mdl','CMdl')

Name Size Bytes Class Attributes

CMdl 1x1 5534 classreg.learning.classif.CompactClassificationNaiveBayes
Mdl 1x1 12907 ClassificationNaiveBayes

CMdl is a CompactClassificationNaiveBayes classifier. It uses less memory than Mdl because Mdl stores the data.

Display the class names of CMdl using dot notation.

ans = 3×1 cell {'setosa' } {'versicolor'} {'virginica' }

Predict the labels. Estimate the posterior probabilities and expected class misclassification costs.

[labels,PostProbs,MisClassCost] = predict(CMdl,XNew);

Compare the true labels with the predicted labels.

table(YNew,labels,PostProbs,MisClassCost,'VariableNames',... {'TrueLabels','PredictedLabels',... 'PosteriorProbabilities','MisclassificationCosts'})

ans=10×4 table TrueLabels PredictedLabels PosteriorProbabilities MisclassificationCosts
______________ _______________ _________________________________________ ______________________________________

{'virginica' }    {'virginica' }     4.0832e-268     4.6422e-09              1             1             1    4.6422e-09
{'setosa'    }    {'setosa'    }               1     3.0706e-18     4.6719e-25    3.0706e-18             1             1
{'virginica' }    {'virginica' }     1.0007e-246     5.8758e-10              1             1             1    5.8758e-10
{'versicolor'}    {'versicolor'}      1.2022e-61        0.99995     4.9859e-05             1    4.9859e-05       0.99995
{'virginica' }    {'virginica' }      2.687e-226     1.7905e-08              1             1             1    1.7905e-08
{'versicolor'}    {'versicolor'}      3.3431e-76        0.99971     0.00028983             1    0.00028983       0.99971
{'virginica' }    {'virginica' }       4.05e-166      0.0028527        0.99715             1       0.99715     0.0028527
{'setosa'    }    {'setosa'    }               1     1.1272e-14     2.0308e-23    1.1272e-14             1             1
{'virginica' }    {'virginica' }     1.3292e-228     8.3604e-10              1             1             1    8.3604e-10
{'setosa'    }    {'setosa'    }               1     4.5023e-17     2.1724e-24    4.5023e-17             1             1

PostProbs and MisClassCost are 10-by-3 numeric matrices, where each row corresponds to a new observation and each column corresponds to a class. The order of the columns corresponds to the order of CMdl.ClassNames.

Load the fisheriris data set. Create X as a numeric matrix that contains petal length and width measurements for 150 irises. Create Y as a cell array of character vectors that contains the corresponding iris species.

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

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.

Define a grid of values in the observed predictor space.

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

Predict the posterior probabilities for each instance in the grid.

[~,PosteriorRegion] = predict(Mdl,[x1Grid(:),x2Grid(:)]);

Plot the posterior probability regions and the training data.

h = scatter(x1Grid(:),x2Grid(:),1,PosteriorRegion); h.MarkerEdgeAlpha = 0.3;

Plot the data.

hold on gh = gscatter(X(:,1),X(:,2),Y,'k','dx*'); title 'Iris Petal Measurements and Posterior Probabilities'; xlabel 'Petal length (cm)'; ylabel 'Petal width (cm)'; axis tight legend(gh,'Location','Best') hold off

Input Arguments

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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 that make up the columns ofX must have the same order as the predictor variables that trainedMdl.
  • If you train Mdl using a table (for example, Tbl), thenX can be a numeric matrix ifTbl contains only numeric predictor variables. To treat numeric predictors inTbl as categorical during training, identify categorical predictors using the'CategoricalPredictors' name-value pair argument of fitcnb. If Tbl contains heterogeneous predictor variables (for example, numeric and categorical data types) and X is a numeric matrix, then predict throws an error.
• For a table:
  • predict does not support multicolumn variables or cell arrays other than cell arrays of character vectors.
  • If you train Mdl using a table (for example, Tbl), then all predictor variables in X must have the same variable names and data types as the variables that trained Mdl (stored inMdl.PredictorNames). However, the column order of X does not need to correspond to the column order ofTbl. Tbl andX can contain additional variables (response variables, observation weights, and so on), but predict ignores them.
  • If you train Mdl using a numeric matrix, then the predictor names inMdl.PredictorNames must be the same as the corresponding predictor variable names inX. To specify predictor names during training, use the 'PredictorNames' name-value pair argument of fitcnb. All predictor variables in X must be numeric vectors.X can contain additional variables (response variables, observation weights, and so on), but predict ignores them.

Data Types: table | double | single

Notes:

• If Mdl.DistributionNames is 'mn', then the software returns NaNs corresponding to rows ofX that contain at least one NaN.
• If Mdl.DistributionNames is not'mn', then the software ignores NaN values when estimating misclassification costs and posterior probabilities. Specifically, the software computes the conditional density of the predictors given the class by leaving out the factors corresponding to missing predictor values.
• For predictor distribution specified as 'mvmn', ifX contains levels that are not represented in the training data (that is, not in Mdl.CategoricalLevels for that predictor), then the conditional density of the predictors given the class is 0. For those observations, the software returns the corresponding value of Posterior as a NaN. The software determines the class label for such observations using the class prior probability stored in Mdl.Prior.

Output Arguments

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

The predicted class labels have the following:

• Same data type as the observed class labels (Mdl.Y). (The software treats string arrays as cell arrays of character vectors.)
• Length equal to the number of rows of Mdl.X.
• Class yielding the lowest expected misclassification cost (Cost).

Class Posterior Probability, returned as a numeric matrix.Posterior has rows equal to the number of rows ofMdl.X and columns equal to the number of distinct classes in the training data (size(Mdl.ClassNames,1)).

Posterior(j,k) is the predicted posterior probability of classk (in class Mdl.ClassNames(k)) given the observation in row j of Mdl.X.

Expected Misclassification Cost, returned as a numeric matrix.Cost has rows equal to the number of rows ofMdl.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 rowj of Mdl.X predicted into classk (in class Mdl.ClassNames(k)).

More About

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A misclassification cost is the relative severity of a classifier labeling an observation into the wrong class.

Two types of misclassification cost exist: true and expected. Let K be the number of classes.

• True misclassification cost — A_K_-by-K matrix, where element (i,j) indicates the cost of classifying an observation into class j if its true class is_i_. The software stores the misclassification cost in the property Mdl.Cost, and uses it in computations. By default,Mdl.Cost(i,j) = 1 if i ≠j, and Mdl.Cost(i,j) = 0 ifi = j. In other words, the cost is0 for correct classification and 1 for any incorrect classification.
• Expected misclassification cost — A_K_-dimensional vector, where element k is the weighted average cost of classifying an observation into class_k_, weighted by the class posterior probabilities.
  In other words, the software classifies observations into the class with the lowest expected misclassification cost.

The posterior probability is the probability that an observation belongs in a particular class, given the data.

For naive Bayes, the posterior probability that a classification is k for a given observation (_x_1,...,xP) is

where:

• P(X1,...,XP|y=k) is the conditional joint density of the predictors given they are in class k. Mdl.DistributionNames stores the distribution names of the predictors.
• π(Y = k) is the class prior probability distribution. Mdl.Prior stores the prior distribution.
• P(X1,..,XP) is the joint density of the predictors. The classes are discrete, so P(X1,...,XP)=∑k=1KP(X1,...,XP|y=k)π(Y=k).

The prior probability of a class is the assumed relative frequency with which observations from that class occur in a population.

Alternative Functionality

Simulink Block

To integrate the prediction of a naive Bayes classification model into Simulink®, you can use the ClassificationNaiveBayes Predict block in the Statistics and Machine Learning Toolbox™ library or a MATLAB® Function block with the predict function. For examples, see Predict Class Labels Using ClassificationNaiveBayes Predict Block and Predict Class Labels Using MATLAB Function Block.

When deciding which approach to use, consider the following:

• If you use the Statistics and Machine Learning Toolbox library block, you can use the Fixed-Point Tool (Fixed-Point Designer) to convert a floating-point model to fixed point.
• Support for variable-size arrays must be enabled for a MATLAB Function block with the predict function.
• If you use a MATLAB Function block, you can use MATLAB functions for preprocessing or post-processing before or after predictions in the same MATLAB Function block.

Extended Capabilities

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This function fully supports tall arrays. You can use models trained on either in-memory or tall data with this function.

For more information, see Tall Arrays.

Usage notes and limitations:

• Use saveLearnerForCoder, loadLearnerForCoder, and codegen (MATLAB Coder) to generate code for the predict function. Save a trained model by using saveLearnerForCoder. Define an entry-point function that loads the saved model by using loadLearnerForCoder and calls thepredict function. Then use codegen to generate code for the entry-point function.
• To generate single-precision C/C++ code forpredict, specify DataType="single" when you call the loadLearnerForCoder function.
• 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 theCompactClassificationNaiveBayes object.
  X	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 in X 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 R2014b