(Not recommended) Perform linear regression of shallow network outputs on targets
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.
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
t — Target
matrix | cell array
Network targets, specified as a matrix or cell array.
y — Output
Network outputs, specified as a matrix or cell array.
r — Regression value
Regression value, returned as a scalar.
m — Slope
Slope of regression fit, returned as a scalar.
b — Offset
Offset of regression fit, returned as a scalar.