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.
npx skills add ECNU-ICALK/AutoSkill --skill matlab-regression-model-comparison-and-visualization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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` i
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What does the MATLAB Regression Model Comparison and Visualization skill do?
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.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill matlab-regression-model-comparison-and-visualization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.