# Regression Ml

> Build ML regression models: gradient boosting, random forest, elastic net, SVR, and KNN regression. Includes model comparison, residual analysis, and prediction intervals. Use when predicting continuous outcomes with complex non-linear relationships.

- **Type:** Skill
- **Install:** `agentstack add skill-ericwang915-data-scientist-skills-regression-ml`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [ericwang915](https://agentstack.voostack.com/s/ericwang915)
- **Installs:** 0
- **Category:** [Productivity](https://agentstack.voostack.com/c/productivity)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [ericwang915](https://github.com/ericwang915)
- **Source:** https://github.com/ericwang915/data-scientist-skills/tree/main/ds-machine-learning/skills/regression-ml

## Install

```sh
agentstack add skill-ericwang915-data-scientist-skills-regression-ml
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# ML Regression

## Purpose
Build regression models using machine learning algorithms for problems where linear regression is insufficient.

## How It Works

### Step 1: Baseline
- Simple linear regression as baseline metric
- Evaluate: RMSE, MAE, R², MAPE

### Step 2: Train ML Models
- **Elastic Net**: Regularized linear (handles multicollinearity)
- **Random Forest**: Non-linear, robust, feature importance
- **XGBoost/LightGBM**: Best accuracy on tabular data
- **SVR**: Kernel-based, good for small datasets
- Cross-validation with shuffled or time-based splits

### Step 3: Evaluate
- Metrics: RMSE, MAE, MAPE, R² on holdout set
- Residual analysis: patterns, heteroscedasticity
- Prediction intervals (quantile regression or conformal prediction)
- Learning curves for bias-variance diagnosis

### Step 4: Interpret
- Feature importance (SHAP, permutation, gain)
- Partial dependence plots
- Residual vs. predicted plots

## Usage Examples

```
"Predict monthly revenue using marketing spend, seasonality, and market data"
```

```
"Build a house price estimator with prediction intervals"
```

## Output Format

- **Model Comparison**: Metrics table across models
- **Best Model**: Hyperparameters and performance metrics
- **Feature Importance**: SHAP summary plot
- **Predictions**: With confidence/prediction intervals
- **Python Code**: sklearn / XGBoost pipeline

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [ericwang915](https://github.com/ericwang915)
- **Source:** [ericwang915/data-scientist-skills](https://github.com/ericwang915/data-scientist-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-ericwang915-data-scientist-skills-regression-ml
- Seller: https://agentstack.voostack.com/s/ericwang915
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.