How to find the final formula or equation that is produced by regression learner.

Question

Yaser Khojah 2019-5-14

2
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/462079-how-to-find-the-final-formula-or-equation-that-is-produced-by-regression-learner

回答： Ayush Aniket 2025-6-11

I have created a trainedModel for my data by using Train Regression Models in Regression Learner App. After I exported my model to workspace, I'm looking for a way to write my equation on a paper as y = variable1......Variable 16. The reason I need this is to see how these variables are correlated. Is there any way to study this? I see the trainedModel structure but I can’t see the equation in terms of formal. Any idea please?

I have this:

yfityfit = trainedModel.predictFcn(T)
%% but I wanna see the equation inside the trainedModel.predictFcn
rainedModel
trainedModel = 
  struct with fields:
        predictFcn: @(x)exportableModel.predictFcn(predictorExtractionFcn(x))
    RegressionTree: [1×1 classreg.learning.regr.CompactRegressionTree]
             About: 'This struct is a trained model exported from Regression Learner R2019a.'
      HowToPredict: 'To make predictions on a new predictor column matrix, X, use: ↵  yfit = c.predictFcn(X) ↵replacing 'c' with the name of the variable that is this struct, e.g. 'trainedModel'. ↵ ↵X must contain exactly 16 columns because this model was trained using 16 predictors. ↵X must contain only predictor columns in exactly the same order and format as your training ↵data. Do not include the response column or any columns you did not import into the app. ↵ ↵For more information, see How to predict using an exported model.'

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Samuel Ferreira 2022-4-23

Hey, I'm have the same problem. I want get the equation of the regression learning after training. Some answer for it?

Nathan Williams 2022-7-28

I also have the same problem.

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Answer 1

Ayush Aniket 2025-6-11

0
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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/462079-how-to-find-the-final-formula-or-equation-that-is-produced-by-regression-learner#answer_1566272

在 MATLAB Online 中打开

You can extract the equation from your trained model by accessing its coefficients and predictor names. Since trainedModel.predictFcn is a function handle, it doesn't directly expose the equation. However, if your model is a linear regression model, you can retrieve the coefficients as show below:

% Generate synthetic data
rng(42); % Set random seed for reproducibility
X = randn(100, 3); % 100 samples, 3 predictors
Y = 2*X(:,1) - 3*X(:,2) + 1.5*X(:,3) + randn(100,1)*0.5; % Linear relationship with noise
% Convert to table format for Regression Learner App
dataTable = array2table([X Y], 'VariableNames', {'Var1', 'Var2', 'Var3', 'Response'});

I am using fitlm function for demo purpose, you can replace this step by exporting the trained model from the Regression Learner App.

% Train a linear regression model using fitlm
trainedModel = fitlm(dataTable, 'Response ~ Var1 + Var2 + Var3');
coefficients = trainedModel.Coefficients.Estimate;
predictorNames = trainedModel.PredictorNames;
% Construct the regression equation
equation = "y = " + string(coefficients(1)); % Intercept
for i = 2:length(coefficients)
    equation = equation + " + " + string(coefficients(i)) + "*" + predictorNames{i-1};
end
disp('Extracted Regression Equation:');
Extracted Regression Equation:
disp(equation);
y = 0.036954 + 2.0447*Var1 + -3.0592*Var2 + 1.4263*Var3