regression
(Not recommended) Perform linear regression of shallow network outputs on targets
regression is not recommended. Use fitlm (Statistics and Machine Learning Toolbox)
instead. For more information, see Compatibility Considerations.
Description
[
calculates the linear regression between each element of the network response and the
corresponding target. r,m,b] = regression(t,y)
This function takes cell array or matrix target t and output y, each with total matrix rows of N, and returns the regression values, r, the slopes of regression fit, m, and the y-intercepts, b, for each of the N matrix rows.
Examples
Fit Regression Model and Plot Fitted Values versus Targets
This example shows how to train a feedforward network and calculate and plot the regression between its targets and outputs.
Load the training data.
[x,t] = simplefit_dataset;
The 1-by-94 matrix x contains the input values and the 1-by-94 matrix t contains the associated target output values.
Construct a feedforward neural network with one hidden layer of size 20.
net = feedforwardnet(20);
Train the network net using the training data.
net = train(net,x,t);
Estimate the targets using the trained network.
y = net(x);
Calculate and plot the regression between its targets and outputs.
[r,m,b] = regression(t,y)
r = 1.0000
m = 1.0000
b = 1.0878e-04
plotregression(t,y)
Input Arguments
Output Arguments
Compatibility Considerations
See Also
confusion | plotregression | fitlm (Statistics and Machine Learning Toolbox)