Main Content

resubLoss

Resubstitution regression loss

collapse all in page

    Syntax

    L = resubLoss(Mdl)
    L = resubLoss(Mdl,Name=Value)

    Description

    L = resubLoss(Mdl) returns the regression loss by resubstitution (L), or the in-sample regression loss, for the trained regression model Mdl using the training data stored in Mdl.X and the corresponding responses stored in Mdl.Y.

    The interpretation of L depends on the loss function (LossFun) and weighting scheme (Mdl.W). In general, better models yield smaller loss values. The default LossFun value is "mse" (mean squared error).

    example

    L = resubLoss(Mdl,Name=Value) specifies additional options using one or more name-value arguments. For example, IncludeInteractions=false specifies to exclude interaction terms from a generalized additive model Mdl.

    example

    Examples

    collapse all

    Open Live Script

    Train a generalized additive model (GAM), then calculate the resubstitution loss using the mean squared error (MSE).

    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.

    Mdl = fitrgam(tbl,"Systolic")
    Mdl = 
  RegressionGAM
            PredictorNames: {'Age'  'Diastolic'  'Smoker'  'Weight'  'Gender'  'SelfAssessedHealthStatus'}
              ResponseName: 'Systolic'
     CategoricalPredictors: [3 5 6]
         ResponseTransform: 'none'
                 Intercept: 122.7800
    IsStandardDeviationFit: 0
           NumObservations: 100

    Mdl is a RegressionGAM model object.

    Calculate the resubstitution loss using the mean squared error (MSE).

    L = resubLoss(Mdl)
    L = 
4.1957
    Open Live Script

    Load the sample data and store in a table. 

    load fisheriris
tbl = table(meas(:,1),meas(:,2),meas(:,3),meas(:,4),species,...
'VariableNames',{'meas1','meas2','meas3','meas4','species'});

    Fit a GPR model using the first measurement as the response and the other variables as the predictors. 

    mdl = fitrgp(tbl,'meas1');

    Predict the responses using the trained model. 

    ypred = predict(mdl,tbl);

    Compute the mean absolute error. 

    n = height(tbl);
y = tbl.meas1;
fun = @(y,ypred,w) sum(abs(y-ypred))/n;
L = resubLoss(mdl,'lossfun',fun)
    L = 
0.2345
    Open Live Script

    Train a generalized additive model (GAM) that contains both linear and interaction terms for predictors, and estimate the regression loss (mean squared error, MSE) with and without interaction terms for the training data and test data. Specify whether to include interaction terms when estimating the regression loss.

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

    Train a generalized additive model 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.

    Compute the resubstitution MSEs (that is, the in-sample MSEs) both with and without interaction terms in Mdl. To exclude interaction terms, specify 'IncludeInteractions',false.

    resubl = resubLoss(Mdl)
    resubl = 
0.0292

    resubl_nointeraction = resubLoss(Mdl,'IncludeInteractions',false)
    resubl_nointeraction = 
4.7330

    Compute the regression MSEs both with and without interaction terms for the test data set. Use a memory-efficient model object for the computation.

    CMdl = compact(Mdl);

    CMdl is a CompactRegressionGAM model object.

    l = loss(CMdl,XNew,YNew)
    l = 
12.8604

    l_nointeraction = loss(CMdl,XNew,YNew,'IncludeInteractions',false)
    l_nointeraction = 
15.6741

    Including interaction terms achieves a smaller error for the training data set and test data set.

    Input Arguments

    collapse all

    Regression machine learning model, specified as a full regression model object, as given in the following table of supported models.

    ModelRegression Model Object
    Gaussian process regression modelRegressionGP
    Generalized additive model (GAM)RegressionGAM
    Neural network modelRegressionNeuralNetwork

    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: resubLoss(Mdl,IncludeInteractions=false) excludes interaction terms from a generalized additive model Mdl.

    Flag to include interaction terms of the model, specified as true or false. This argument is valid only for a generalized additive model. That is, you can specify this argument only when Mdl is RegressionGAM.

    The default 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

    Loss function, specified as "mse" or a function handle.

    • "mse" — Weighted mean squared error.

    • Function handle — To specify a custom loss function, use a function handle. The function must have this form:

      lossval = lossfun(Y,YFit,W)

      • The output argument lossval is a floating-point scalar.

      • You specify the function name (lossfun).

      • If Mdl is a model with one response variable, then Y is a length-n numeric vector of observed responses, where n is the number of observations in Tbl or X. If Mdl is a model with multiple response variables, then Y is an n-by-k numeric matrix of observed responses, where k is the number of response variables.

      • YFit is a length-n numeric vector or an n-by-k numeric matrix of corresponding predicted responses. The size of YFit must match the size of Y.

      • W is an n-by-1 numeric vector of observation weights.

    Example: LossFun=@lossfun

    Data Types: char | string | function_handle

    Since R2024b

    Type of output loss, specified as "average" or "per-response". This argument is valid only for a neural network model. That is, you can specify this argument only when Mdl is a RegressionNeuralNetwork object.

    ValueDescription
    "average"resubLoss averages the loss values across all response variables and returns a scalar value.
    "per-response"resubLoss returns a vector, where each element is the loss for one response variable.

    Example: OutputType="per-response"

    Data Types: char | string

    Since R2023b

    Predicted response value to use for observations with missing predictor values, specified as "median", "mean", "omitted", or a numeric scalar. This argument is valid only for a Gaussian process regression or neural network model. That is, you can specify this argument only when Mdl is a RegressionGP or RegressionNeuralNetwork object.

    ValueDescription
    "median"

    resubLoss uses the median of the observed response values in the training data as the predicted response value for observations with missing predictor values.

    This value is the default when Mdl is a RegressionGP or RegressionNeuralNetwork object.

    "mean"resubLoss uses the mean of the observed response values in the training data as the predicted response value for observations with missing predictor values.
    "omitted"resubLoss excludes observations with missing predictor values from the loss computation.
    Numeric scalarresubLoss uses this value as the predicted response value for observations with missing predictor values.

    If an observation is missing an observed response value or an observation weight, then resubLoss does not use the observation in the loss computation.

    Example: PredictionForMissingValue="omitted"

    Data Types: single | double | char | string

    Since R2024b

    Flag to standardize the response data before computing the loss, specified as a numeric or logical 0 (false) or 1 (true). This argument is valid only for a neural network model. That is, you can specify this argument only when Mdl is a RegressionNeuralNetwork object.

    If you set StandardizeResponses to true, then the software centers and scales each response variable in Mdl.Y by the corresponding column mean and standard deviation. Specify StandardizeResponses as true when you have multiple response variables with very different scales and OutputType is "average". Do not standardize the response data when you have only one response variable.

    Example: StandardizeResponses=true

    Data Types: single | double | logical

    Output Arguments

    collapse all

    Regression loss, returned as a numeric scalar or vector. The type of regression loss depends on LossFun.

    When Mdl is a model with one response variable, L is a numeric scalar. When Mdl is a model with multiple response variables, the size and interpretation of L depend on OutputType.

    More About

    collapse all

    Weighted Mean Squared Error

    The weighted mean squared error measures the predictive inaccuracy of regression models. When you compare the same type of loss among many models, a lower error indicates a better predictive model.

    The weighted mean squared error is calculated as follows:

    mse=∑j=1nwj(f(xj)−yj)2∑j=1nwj ,

    where:

    • n is the number of rows of data.

    • xj is the jth row of data.

    • yj is the true response to xj.

    • f(xj) is the response prediction of the model Mdl to xj.

    • w is the vector of observation weights.

    Algorithms

    resubLoss computes the regression loss according to the corresponding loss function of the object (Mdl). For a model-specific description, see the loss function reference pages in the following table.

    ModelRegression Model Object (Mdl)loss Object Function
    Gaussian process regression modelRegressionGPloss
    Generalized additive modelRegressionGAMloss
    Neural network modelRegressionNeuralNetworkloss

    Alternative Functionality

    To compute the response loss for new predictor data, use the corresponding loss function of the object (Mdl).

    Extended Capabilities

    Version History

    Introduced in R2015b

    expand all

    See Also