# Retrieving incremental regression kernel trained model

조회 수: 13 (최근 30일)
Yasmine 2024년 6월 24일
편집: Yasmine 2024년 7월 29일 17:37
How can I retrieve the incremental regression kernel trained model?: the equation that predict function uses to predict output.

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### 답변 (1개)

Shubham 2024년 6월 26일
Hi Yasmine,
To retrieve and understand the incremental regression kernel model in MATLAB, you can use the incrementalRegressionKernel object. This object allows you to perform online learning with kernel regression models, and you can extract information about the model parameters to understand the prediction equation. Here is the documentation link: https://in.mathworks.com/help/stats/incrementalregressionkernel.html
Steps to Retrieve the Model and Understand the Prediction Equation:
1. Train the Incremental Regression Kernel Model:
• You can train the model incrementally using the incrementalRegressionKernel object and the fit function.
2. Retrieve Model Parameters:
• Once the model is trained, you can access its parameters, including the support vectors and the corresponding coefficients.
% Extract support vectors and coefficients
supportVectors = model.SupportVectors;
alpha = model.Alpha;
bias = model.Bias;
kernelFunction = model.KernelFunction;
% Display the kernel function and parameters
disp(['Kernel Function: ', func2str(kernelFunction)]);
disp(['Support Vectors: ', num2str(supportVectors)]);
disp(['Alpha Coefficients: ', num2str(alpha)]);
disp(['Bias: ', num2str(bias)]);
3. Construct the Prediction Equation:
• The prediction equation for a kernel regression model typically involves a kernel function applied to the support vectors and the input data, weighted by the model coefficients.
• The prediction function in kernel regression is typically: Where are the coefficients, is the kernel function applied to the input and support vectors , and b is the bias term. Using the extracted parameters, you can construct this equation in MATLAB.
I hope this helps!
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Yasmine 2024년 7월 25일 12:34
편집: Yasmine 2024년 7월 29일 17:37
The coomands you mention for retrieving alpha and bias do not work. These are not explicit properties of the model

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