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Classify observations using generalized additive model (GAM)

returns a vector of Predicted Class Labels for the predictor data in
the table or matrix `label`

= predict(`Mdl`

,`X`

)`X`

, based on the generalized additive model
`Mdl`

for binary classification. The trained model can be either full
or compact.

For each observation in `X`

, the predicted class label corresponds to
the minimum Expected Misclassification Cost.

specifies whether to include interaction terms in computations.`label`

= predict(`Mdl`

,`X`

,'IncludeInteractions',`includeInteractions`

)

Train a generalized additive model using training samples, and then label the test samples.

Load the `fisheriris`

data set. Create `X`

as a numeric matrix that contains sepal and petal measurements for versicolor and virginica irises. Create `Y`

as a cell array of character vectors that contains the corresponding iris species.

load fisheriris inds = strcmp(species,'versicolor') | strcmp(species,'virginica'); X = meas(inds,:); Y = species(inds,:);

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.

rng('default') % For reproducibility 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 generalized additive model using the predictors `XTrain`

and class labels `YTrain`

. A recommended practice is to specify the class names.

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

Mdl = ClassificationGAM ResponseName: 'Y' CategoricalPredictors: [] ClassNames: {'versicolor' 'virginica'} ScoreTransform: 'logit' Intercept: -1.1090 NumObservations: 70 Properties, Methods

`Mdl`

is a `ClassificationGAM`

model object.

Predict the test sample labels.

label = predict(Mdl,XTest);

Create a table containing the true labels and predicted labels. Display the table for a random set of 10 observations.

t = table(YTest,label,'VariableNames',{'True Label','Predicted Label'}); idx = randsample(sum(testInds),10); t(idx,:)

`ans=`*10×2 table*
True Label Predicted Label
______________ _______________
{'virginica' } {'virginica' }
{'virginica' } {'virginica' }
{'versicolor'} {'virginica' }
{'virginica' } {'virginica' }
{'virginica' } {'virginica' }
{'versicolor'} {'versicolor'}
{'versicolor'} {'versicolor'}
{'versicolor'} {'versicolor'}
{'versicolor'} {'versicolor'}
{'virginica' } {'virginica' }

Create a confusion chart from the true labels `YTest`

and the predicted labels `label`

.

cm = confusionchart(YTest,label);

Estimate the logit of posterior probabilities for new observations using a classification GAM that contains both linear and interaction terms for predictors. Classify new observations using a memory-efficient model object. Specify whether to include interaction terms when classifying new observations.

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 two sets: one containing training data, and the other containing new, unobserved test data. Reserve 10 observations for the new test data set.

rng('default') % For reproducibility n = size(X,1); newInds = randsample(n,10); inds = ~ismember(1:n,newInds); XNew = X(newInds,:); YNew = Y(newInds);

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(inds,:),Y(inds),'ClassNames',{'b','g'},'Interactions',10);

`Mdl`

is a `ClassificationGAM`

model object.

Conserve memory by reducing the size of the trained model.

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

Name Size Bytes Class Attributes CMdl 1x1 1081082 classreg.learning.classif.CompactClassificationGAM Mdl 1x1 1282640 ClassificationGAM

`CMdl`

is a `CompactClassificationGAM`

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'`

.

CMdl.ScoreTransform = 'none'; [labels,scores] = predict(CMdl,XNew); [labels_nointeraction,scores_nointeraction] = predict(CMdl,XNew,'IncludeInteractions',false); t = table(YNew,labels,scores,labels_nointeraction,scores_nointeraction, ... 'VariableNames',{'True Labels','Predicted Labels','Scores' ... 'Predicted Labels Without Interactions','Scores Without Interactions'})

`t=`*10×5 table*
True Labels Predicted Labels Scores Predicted Labels Without Interactions Scores Without Interactions
___________ ________________ __________________ _____________________________________ ___________________________
{'g'} {'g'} -40.23 40.23 {'g'} -37.484 37.484
{'g'} {'g'} -41.215 41.215 {'g'} -38.737 38.737
{'g'} {'g'} -44.413 44.413 {'g'} -42.186 42.186
{'g'} {'b'} 3.0658 -3.0658 {'b'} 1.4338 -1.4338
{'g'} {'g'} -84.637 84.637 {'g'} -81.269 81.269
{'g'} {'g'} -27.44 27.44 {'g'} -24.831 24.831
{'g'} {'g'} -62.989 62.989 {'g'} -60.4 60.4
{'g'} {'g'} -77.109 77.109 {'g'} -75.937 75.937
{'g'} {'g'} -48.519 48.519 {'g'} -47.067 47.067
{'g'} {'g'} -56.256 56.256 {'g'} -53.373 53.373

The predicted labels for the test data `Xnew`

do not vary depending on the inclusion of interaction terms, but the estimated score values are different.

Train a generalized additive model, and then plot the posterior probability regions using the probability values of the first class.

Load the `fisheriris`

data set. Create `X`

as a numeric matrix that contains two petal measurements for versicolor and virginica irises. Create `Y`

as a cell array of character vectors that contains the corresponding iris species.

load fisheriris inds = strcmp(species,'versicolor') | strcmp(species,'virginica'); X = meas(inds,3:4); Y = species(inds,:);

Train a generalized additive model using the predictors `X`

and class labels `Y`

. A recommended practice is to specify the class names.

Mdl = fitcgam(X,Y,'ClassNames',{'versicolor','virginica'});

`Mdl`

is a `ClassificationGAM`

model object.

Define a grid of values in the observed predictor space.

xMax = max(X); xMin = min(X); x1 = linspace(xMin(1),xMax(1),250); x2 = linspace(xMin(2),xMax(2),250); [x1Grid,x2Grid] = meshgrid(x1,x2);

Predict the posterior probabilities for each instance in the grid.

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

Plot the posterior probability regions using the probability values of the first class `'versicolor'`

.

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

Plot the training 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)') legend(gh,'Location','Best') colorbar hold off

`Mdl`

— Generalized additive model`ClassificationGAM`

model object | `CompactClassificationGAM`

model objectGeneralized additive model, specified as a `ClassificationGAM`

or `CompactClassificationGAM`

model object.

`X`

— Predictor datanumeric matrix | table

Predictor data, 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 of

`X`

must have the same order as the predictor variables that trained`Mdl`

.If you trained

`Mdl`

using a table, then`X`

can be a numeric matrix if the table contains all numeric predictor variables.

For a table:

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 in`Tbl`

. However, the column order of`X`

does not need to correspond to the column order of`Tbl`

.If you trained

`Mdl`

using a numeric matrix, then the predictor names in`Mdl.PredictorNames`

and the corresponding predictor variable names in`X`

must be the same. To specify predictor names during training, use the`'PredictorNames'`

name-value argument. 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.`predict`

does not support multicolumn variables or cell arrays other than cell arrays of character vectors.

**Data Types: **`table`

| `double`

| `single`

`includeInteractions`

— Flag to include interaction terms`true`

| `false`

Flag to include interaction terms of the model, specified as `true`

or
`false`

.

The default `includeInteractions`

value is `true`

if `Mdl`

contains interaction terms. The value must be `false`

if the model does not contain interaction terms.

**Data Types: **`logical`

`label`

— Predicted class labelscategorical 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.

If `Mdl.ScoreTransform`

is `'logit'`

(default),
then each entry of `label`

corresponds to the class with the minimal
Expected Misclassification Cost for the corresponding
row of `X`

. Otherwise, each entry corresponds to the class with the
maximal score.

`label`

has the same data type as the observed class labels that
trained `Mdl`

, and its length is equal to the number of rows in
`X`

. (The software treats string arrays as cell arrays of character
vectors.)

`score`

— Predicted posterior probabilities or class scorestwo-column numeric matrix

Predicted posterior probabilities or class scores, returned as a two-column numeric
matrix with the same number of rows as `X`

. The first and second
columns of `score`

contain the first class (or negative class,
`Mdl.ClassNames(1)`

) and second class (or positive class,
`Mdl.ClassNames(2)`

) score values for the corresponding observations,
respectively.

If `Mdl.ScoreTransform`

is `'logit'`

(default),
then the score values are posterior probabilities. If
`Mdl.ScoreTransform`

is `'none'`

, then the score
values are the logit of posterior probabilities. The software provides several built-in
score transformation functions. For more details, see the `ScoreTransform`

property of `Mdl`

.

You can change the score transformation by specifying the `'ScoreTransform'`

argument of `fitcgam`

during training,
or by changing the `ScoreTransform`

property after training.

`predict`

classifies by minimizing the expected
misclassification cost:

$$\widehat{y}=\underset{y=1,\mathrm{...},K}{\mathrm{arg}\mathrm{min}}{\displaystyle \sum _{j=1}^{K}\widehat{P}\left(j|x\right)C\left(y|j\right)},$$

where:

$$\widehat{y}$$ is the predicted classification.

*K*is the number of classes.$$\widehat{P}\left(j|x\right)$$ is the posterior probability of class

*j*for observation*x*.$$C\left(y|j\right)$$ is the cost of classifying an observation as

*y*when its true class is*j*.

The expected misclassification cost per observation is an averaged cost of classifying the observation into each class.

Suppose you have `Nobs`

observations that you want to classify with a trained
classifier, and you have `K`

classes. You place the observations
into a matrix `X`

with one observation per row.

The expected cost matrix `CE`

has size
`Nobs`

-by-`K`

. Each row of
`CE`

contains the expected (average) cost of classifying
the observation into each of the `K`

classes.
`CE(`

is*n*,*k*)

$$\sum _{i=1}^{K}\widehat{P}\left(i|X(n)\right)C\left(k|i\right)},$$

where:

*K*is the number of classes.$$\widehat{P}\left(i|X(n)\right)$$ is the posterior probability of class

*i*for observation*X*(*n*).$$C\left(k|i\right)$$ is the true misclassification cost of classifying an observation as

*k*when its true class is*i*.

The true misclassification cost is the cost of classifying an observation into an incorrect class.

You can set the true misclassification cost per class by using the `'Cost'`

name-value argument when you create the classifier. `Cost(i,j)`

is the cost
of classifying an observation into class `j`

when its true class is
`i`

. By default, `Cost(i,j)=1`

if
`i~=j`

, and `Cost(i,j)=0`

if `i=j`

.
In other words, the cost is `0`

for correct classification and
`1`

for incorrect classification.

`edge`

| `loss`

| `margin`

| `resubPredict`

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