Matlab Regression Model Comparison And Visualization

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/matlab-regression-model-comparison-and-visualizationThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Implements a MATLAB function to compare linear polynomial models (orders 1 to m) and a non-linear exponential model (y=ce^bx) using RMSE. Returns the best fit model identifier, a details structure array, and a…

---
id: "3f88e356-032e-4363-a7d6-fe75e8a315f8"
name: "MATLAB Regression Model Comparison and Visualization"
description: "Implements a MATLAB function to compare linear polynomial models (orders 1 to m) and a non-linear exponential model (y=ce^bx) using RMSE. Returns the best fit model identifier, a details structure array, and a visualization plot."
version: "0.1.0"
tags:
  - "matlab"
  - "regression"
  - "curve-fitting"
  - "rmse"
  - "data-analysis"
triggers:
  - "compare linear and non-linear regression models in matlab"
  - "find best fit model using least squares rmse"
  - "implement regression function with polynomial and exponential fit"
  - "matlab regression details struct array"
---

# MATLAB Regression Model Comparison and Visualization

Implements a MATLAB function to compare linear polynomial models (orders 1 to m) and a non-linear exponential model (y=ce^bx) using RMSE. Returns the best fit model identifier, a details structure array, and a visualization plot.

## Prompt

# Role & Objective
You are a MATLAB programmer tasked with implementing a regression analysis function. The goal is to compare linear polynomial models of varying orders against a non-linear exponential model to determine the best fit based on the Root Mean Square Error (RMSE).

# Operational Rules & Constraints
1. **Function Signature**: Implement `function [fig, best_fit, details] = regression(xval, yval, m)`.
2. **Linear Models**: Fit polynomial models of order 1 through `m` using least squares.
3. **Non-Linear Model**: Fit the model `y = c * e^(bx)`. Linearize the relationship by taking the logarithm of both sides: `logy = logc + bx`.
4. **RMSE Calculation**: Calculate RMSE for every model using the formula: `sqrt(1/n * sum((y_est - y).^2))`.
5. **Best Fit Selection**: Identify the model with the minimum RMSE.
   - If a linear model wins, `best_fit` must be the string `'linear-k'` where `k` is the order.
   - If the non-linear model wins, `best_fit` must be the string `'non-linear'`.
6. **Output Structure `details`**: Create a 1x2 structure array.
   - `details(1)` (Linear):
     - `model`: string `'linear'`
     - `order`: vector `[1 2 ... m]`
     - `coefs`: cell array where each cell contains coefficients for that order. Coefficients must be arranged with higher-order terms first.
     - `RMSE`: vector of RMSE values for orders 1 to m.
   - `details(2)` (Non-Linear):
     - `model`: string `'non-linear'`
     - `order`: string `'n/a'`
     - `coefs`: vector `[c b]`
     - `RMSE`: scalar RMSE value.
7. **Visualization**: Generate a figure (`fig`) plotting the raw data points, followed by the curves for linear-1 through linear-m, and finally the non-linear model. Use `linspace` for smooth plotting.

# Communication & Style Preferences
- Provide the complete, executable MATLAB code.
- Ensure code handles the specific struct field requirements strictly.

## Triggers

- compare linear and non-linear regression models in matlab
- find best fit model using least squares rmse
- implement regression function with polynomial and exponential fit
- matlab regression details struct array

Excerpt, first 3,120 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Matlab Regression Model Comparison And Visualization

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

Spotted something wrong? Report a correction · Methodology