regression

(不是recommended) Perform linear regression of shallow network outputs on targets

regressionis not recommended. Usefitlm(Statistics and Machine Learning Toolbox)instead. For more information, seeCompatibility Considerations.

Syntax

[r,m,b] = regression(t,y)

[r,m,b] = regression(t,y,'one')

Description

example

[r,m,b] = regression(t,y)calculates the linear regression between each element of the network response and the corresponding target.

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.

[r,m,b] = regression(t,y,'one')combines all matrix rows before regressing, and returns single scalar regression, slope, and offset values.

Examples

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Fit Regression Model and Plot Fitted Values versus Targets

Open Live Script

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 matrixxcontains the input values and the 1-by-94 matrixtcontains the associated target output values.

Construct a feedforward neural network with one hidden layer of size 20.

net = feedforwardnet(20);

Train the networknetusing the training data.

net = train(net,x,t);

Figure Neural Network Training (26-Feb-2022 11:13:59) contains an object of type uigridlayout.

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)

Figure Regression (plotregression) contains an axes object. The axes object with title : R=1 contains 3 objects of type line. These objects represent Y = T, Fit, Data.