resubPredict

Class: RegressionSVM

Predict resubstitution response of support vector machine regression model

Syntax

yfit = resubPredict(mdl)

yfit = resubPredict(mdl,PredictionForMissingValue=prediction)

Description

yfit = resubPredict(mdl) returns a vector of predicted response values, yfit, for the trained support vector machine (SVM) regression model mdl using the predictor data stored in mdl.X.

yfit = resubPredict(mdl,PredictionForMissingValue=prediction) uses the prediction value as the predicted response for observations with missing values in the predictor data mdl.X. By default, resubPredict uses the median of the observed response values in the training data. (since R2023b)

Input Arguments

mdl — Full, trained SVM regression model

RegressionSVM model

Full, trained SVM regression model, specified as a RegressionSVM model returned by fitrsvm.

prediction — Predicted response value to use for observations with missing predictor values

"median" (default) | "mean" | numeric scalar

Since R2023b

Predicted response value to use for observations with missing predictor values, specified as "median", "mean", or a numeric scalar.

Value	Description
"median"	resubPredict uses the median of the observed response values in the training data as the predicted response value for observations with missing predictor values.
"mean"	resubPredict uses the mean of the observed response values in the training data as the predicted response value for observations with missing predictor values.
Numeric scalar	resubPredict uses this value as the predicted response value for observations with missing predictor values.

Example: "mean"

Example: NaN

Data Types: single | double | char | string

Output Arguments

yfit — Predicted response

vector of numeric values

Predicted responses, returned as a vector of numeric values. The length of yfit is equal to the number of observations in the training data, mdl.NumObservations.

For details about how to predict responses, see Equation 1 and Equation 2 in Understanding Support Vector Machine Regression.

Examples

Resubstitution Predictions for SVM Regression Model

This example shows how to train an SVM regression model, then use the model to generate predicted response values from the training data.

This example uses the abalone data from the UCI Machine Learning Repository. Download the data and save it in your current directory with the name 'abalone.data'. Read the data into a table.

tbl = readtable('abalone.data','Filetype','text', ...
    'ReadVariableNames',false);
rng default  % for reproducibility

The sample data contains 4177 observations. All of the predictor variables are continuous except for sex, which is a categorical variable with possible values 'M' (for males), 'F' (for females), and 'I' (for infants). The goal is to predict the number of rings on the abalone, and thereby determine its age, using physical measurements.

Train an SVM regression model to the data, using a Gaussian kernel function with an automatic kernel scale. Standardize the data.

mdl = fitrsvm(tbl,'Var9','KernelFunction','gaussian', ...
    'KernelScale','auto','Standardize',true);

Use the trained model to predict response values based on the original data.

yfit = resubPredict(mdl);

Display the first ten predicted responses alongside the actual response values.

[mdl.Y(1:10),yfit(1:10)]

ans =

   15.0000    8.1836
    7.0000    8.3545
    9.0000   10.9383
   10.0000    9.3446
    7.0000    6.4042
    8.0000    7.7910
   20.0000   13.8275
   16.0000   11.7959
    9.0000    9.5724
   19.0000   13.6909

The left column shows the actual response and the right column shows the corresponding predicted response.

References

[1] Nash, W.J., T. L. Sellers, S. R. Talbot, A. J. Cawthorn, and W. B. Ford. "The Population Biology of Abalone (Haliotis species) in Tasmania. I. Blacklip Abalone (H. rubra) from the North Coast and Islands of Bass Strait." Sea Fisheries Division, Technical Report No. 48, 1994.

[2] Waugh, S. "Extending and Benchmarking Cascade-Correlation: Extensions to the Cascade-Correlation Architecture and Benchmarking of Feed-forward Supervised Artificial Neural Networks." University of Tasmania Department of Computer Science thesis, 1995.

[3] Clark, D., Z. Schreter, A. "Adams. A Quantitative Comparison of Dystal and Backpropagation." submitted to the Australian Conference on Neural Networks, 1996.

[4] Lichman, M. UCI Machine Learning Repository, [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Extended Capabilities

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™. (since R2023a)

This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2015b

R2023b: Specify predicted response value to use for observations with missing predictor values

Starting in R2023b, when you predict or compute the loss, some regression models allow you to specify the predicted response value for observations with missing predictor values. Specify the PredictionForMissingValue name-value argument to use a numeric scalar, the training set median, or the training set mean as the predicted value. When computing the loss, you can also specify to omit observations with missing predictor values.

This table lists the object functions that support the PredictionForMissingValue name-value argument. By default, the functions use the training set median as the predicted response value for observations with missing predictor values.

Model Type	Model Objects	Object Functions
Gaussian process regression (GPR) model	RegressionGP, CompactRegressionGP	loss, predict, resubLoss, resubPredict
	RegressionPartitionedGP	kfoldLoss, kfoldPredict
Gaussian kernel regression model	RegressionKernel	loss, predict
	RegressionPartitionedKernel	kfoldLoss, kfoldPredict
Linear regression model	RegressionLinear	loss, predict
	RegressionPartitionedLinear	kfoldLoss, kfoldPredict
Neural network regression model	RegressionNeuralNetwork, CompactRegressionNeuralNetwork	loss, predict, resubLoss, resubPredict
	RegressionPartitionedNeuralNetwork	kfoldLoss, kfoldPredict
Support vector machine (SVM) regression model	RegressionSVM, CompactRegressionSVM	loss, predict, resubLoss, resubPredict
	RegressionPartitionedSVM	kfoldLoss, kfoldPredict

In previous releases, the regression model loss and predict functions listed above used NaN predicted response values for observations with missing predictor values. The software omitted observations with missing predictor values from the resubstitution ("resub") and cross-validation ("kfold") computations for prediction and loss.

R2023a: GPU array support

Starting in R2023a, resubPredict fully supports GPU arrays.