predict

Predict responses using generalized additive model (GAM)

Since R2021a

Syntax

yFit = predict(Mdl,X)

yFit = predict(Mdl,X,Name,Value)

[yFit,ySD,yInt] = predict(___)

Description

yFit = predict(Mdl,X) returns a vector of predicted responses for the predictor data in the table or matrix X, based on the generalized additive model Mdl for regression. The trained model can be either full or compact.

yFit = predict(Mdl,X,Name,Value) specifies options using one or more name-value arguments. For example, 'IncludeInteractions',true specifies to include interaction terms in computations.

[yFit,ySD,yInt] = predict(___) also returns the standard deviations and prediction intervals of the response variable, evaluated at each observation in the predictor data X, using any of the input argument combinations in the previous syntaxes. This syntax is valid only when you specify 'FitStandardDeviation' of fitrgam as true for training Mdl and the IsStandardDeviationFit property of Mdl is true.

Examples

Predict Test Sample Response

Train a generalized additive model using training samples, and then predict the test sample responses.

Load the patients data set.

load patients

Create a table that contains the predictor variables (Age, Diastolic, Smoker, Weight, Gender, SelfAssessedHealthStatus) and the response variable (Systolic).

tbl = table(Age,Diastolic,Smoker,Weight,Gender,SelfAssessedHealthStatus,Systolic);

Randomly partition observations into a training set and a test set. Specify a 10% holdout sample for testing.

rng('default') % For reproducibility
cv = cvpartition(size(tbl,1),'HoldOut',0.10);

Extract the training and test indices.

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

Train a univariate GAM that contains the linear terms for the predictors in tbl.

Mdl = fitrgam(tbl(trainInds,:),'Systolic')

Mdl = 
  RegressionGAM
            PredictorNames: {'Age'  'Diastolic'  'Smoker'  'Weight'  'Gender'  'SelfAssessedHealthStatus'}
              ResponseName: 'Systolic'
     CategoricalPredictors: [3 5 6]
         ResponseTransform: 'none'
                 Intercept: 122.7444
    IsStandardDeviationFit: 0
           NumObservations: 90


  Properties, Methods

Mdl is a RegressionGAM model object.

Predict responses for the test set.

yFit = predict(Mdl,tbl(testInds,:));

Create a table containing the observed response values and the predicted response values.

table(tbl.Systolic(testInds),yFit, ...
    'VariableNames',{'Observed Value','Predicted Value'})

ans=10×2 table
    Observed Value    Predicted Value
    ______________    _______________

         124              126.58     
         121              123.95     
         130              116.72     
         115              117.35     
         121              117.45     
         116               118.5     
         123              126.16     
         132              124.14     
         125              127.36     
         124              115.99     

Compare Predicted Responses

Predict responses for new observations using a generalized additive model that contains both linear and interaction terms for predictors. Use a memory-efficient model object, and specify whether to include interaction terms when predicting responses.

Load the carbig data set, which contains measurements of cars made in the 1970s and early 1980s.

load carbig

Specify Acceleration, Displacement, Horsepower, and Weight as the predictor variables (X) and MPG as the response variable (Y).

X = [Acceleration,Displacement,Horsepower,Weight];
Y = MPG;

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

Train a GAM that contains all the available linear and interaction terms in X.

Mdl = fitrgam(X(inds,:),Y(inds),'Interactions','all');

Mdl is a RegressionGAM 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             1255766  classreg.learning.regr.CompactRegressionGAM              
  Mdl       1x1             1289882  RegressionGAM                                            

CMdl is a CompactRegressionGAM model object.

Predict the responses using both linear and interaction terms, and then using only linear terms. To exclude interaction terms, specify 'IncludeInteractions',false.

yFit = predict(CMdl,XNew);
yFit_nointeraction = predict(CMdl,XNew,'IncludeInteractions',false);

Create a table containing the observed response values and the predicted response values.

t = table(YNew,yFit,yFit_nointeraction, ...
    'VariableNames',{'Observed Response', ...
    'Predicted Response','Predicted Response Without Interactions'})

t=10×3 table
    Observed Response    Predicted Response    Predicted Response Without Interactions
    _________________    __________________    _______________________________________

          27.9                  23.04                          23.649                 
           NaN                 37.163                          35.779                 
           NaN                 25.876                          21.978                 
            13                 12.786                          14.141                 
            36                 28.889                          27.281                 
          19.9                 22.199                          18.451                 
          24.2                 23.995                          24.885                 
            12                 14.247                          13.982                 
            38                 33.797                          33.528                 
            13                 12.225                          11.127                 

Plot Prediction Intervals

Train a generalized additive model (GAM), and then compute and plot the prediction intervals of response values.

Load the patients data set.

load patients

Create a table that contains the predictor variables (Age, Diastolic, Smoker, Weight, Gender, SelfAssessedHealthStatus) and the response variable (Systolic).

tbl = table(Age,Diastolic,Smoker,Weight,Gender,SelfAssessedHealthStatus,Systolic);

Train a univariate GAM that contains the linear terms for the predictors in tbl. Specify the FitStandardDeviation name-value argument as true so that you can use the trained model to compute prediction intervals. A recommended practice is to use optimal hyperparameters when you fit the standard deviation model for the accuracy of the standard deviation estimates. Specify 'OptimizeHyperparameters' as 'all-univariate'. For reproducibility, use the 'expected-improvement-plus' acquisition function. Specify 'ShowPlots' as false and 'Verbose' as 0 to disable plot and message displays, respectively.

rng('default') % For reproducibility
Mdl = fitrgam(tbl,'Systolic','FitStandardDeviation',true, ...
    'OptimizeHyperparameters','all-univariate', ...
    'HyperparameterOptimizationOptions',struct('AcquisitionFunctionName','expected-improvement-plus', ...
    'ShowPlots',false,'Verbose',0))

Mdl = 
  RegressionGAM
                       PredictorNames: {'Age'  'Diastolic'  'Smoker'  'Weight'  'Gender'  'SelfAssessedHealthStatus'}
                         ResponseName: 'Systolic'
                CategoricalPredictors: [3 5 6]
                    ResponseTransform: 'none'
                            Intercept: 122.7800
               IsStandardDeviationFit: 1
                      NumObservations: 100
    HyperparameterOptimizationResults: [1×1 BayesianOptimization]


  Properties, Methods

Mdl is a RegressionGAM model object that uses the best estimated feasible point. The best estimated feasible point indicates the set of hyperparameters that minimizes the upper confidence bound of the objective function value based on the underlying objective function model of the Bayesian optimization process. For more details on the optimization process, see Optimize GAM Using OptimizeHyperparameters.

Predict responses for the training data in tbl, and compute the 99% prediction intervals of the response variable. Specify the significance level ('Alpha') as 0.01 to set the confidence level of the prediction intervals to 99%.

[yFit,~,yInt] = predict(Mdl,tbl,'Alpha',0.01);

Plot the sorted true responses together with the predicted responses and prediction intervals.

figure
yTrue = tbl.Systolic;
[sortedYTrue,I] = sort(yTrue); 
plot(sortedYTrue,'o')
hold on
plot(yFit(I))
plot(yInt(I,1),'k:')
plot(yInt(I,2),'k:')
legend('True responses','Predicted responses', ...
    'Prediction interval limits','Location','best')
hold off

Input Arguments

Mdl — Generalized additive model

RegressionGAM model object | CompactRegressionGAM model object

Generalized additive model, specified as a RegressionGAM or a CompactRegressionGAM model object.

X — Predictor data

numeric 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

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: 'Alpha',0.01,'IncludeInteractions',false specifies the confidence level as 99% and excludes interaction terms from computations.

Alpha — Significance level

0.05 (default) | numeric scalar in [0,1]

Significance level for the confidence level of the prediction intervals yInt, specified as a numeric scalar in the range [0,1]. The confidence level of yInt is equal to 100(1 – Alpha)%.

This argument is valid only when the IsStandardDeviationFit property of Mdl is true. Specify the 'FitStandardDeviation' name-value argument of fitrgam as true to fit the model for the standard deviation.

Example: 'Alpha',0.01 specifies to return 99% prediction intervals.

Data Types: single | double

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.

Example: 'IncludeInteractions',false

Data Types: logical

Output Arguments

yFit — Predicted responses

column vector

Predicted responses, returned as a column vector of length n, where n is the number of observations in the predictor data X.

ySD — Standard deviations of response variable

column vector

Standard deviations of the response variable, evaluated at each observation in the predictor data X, returned as a column vector of length n, where n is the number of observations in X. The ith element ySD(i) contains the standard deviation of the ith response for the ith observation X(i,:), estimated using the trained standard deviation model in Mdl.

This argument is valid only when the IsStandardDeviationFit property of Mdl is true. Specify the 'FitStandardDeviation' name-value argument of fitrgam as true to fit the model for the standard deviation.

yInt — Prediction intervals of response variable

two-column matrix

Prediction intervals of the response variable, evaluated at each observation in the predictor data X, returned as an n-by-2 matrix, where n is the number of observations in X. The ith row yInt(i,:) contains the 100(1–Alpha)% prediction interval of the ith response for the ith observation X(i,:). The Alpha value is the probability that the prediction interval does not contain the true response value for X(i,:). The first column of yInt contains the lower limits of the prediction intervals, and the second column contains the upper limits.

This argument is valid only when the IsStandardDeviationFit property of Mdl is true. Specify the 'FitStandardDeviation' name-value argument of fitrgam as true to fit the model for the standard deviation.

Algorithms

Standard Deviation and Prediction Interval

predict returns the predicted responses (yFit) and, optionally, the standard deviations (ySD) and prediction intervals (yInt) of the response variable, estimated at each observation in X.

A Generalized Additive Model (GAM) for Regression assumes that the response variable y follows the normal distribution with mean μ and standard deviation σ. If you specify 'FitStandardDeviation' of fitrgam as false (default), then fitrgam trains a model for μ. If you specify 'FitStandardDeviation' as true, then fitrgam trains an additional model for σ and sets the IsStandardDeviationFit property of the GAM object to true. The outputs yFit and ySD correspond to the estimated mean μ and standard deviation σ, respectively.

Version History

Introduced in R2021a