skills.rest
csinva
csinvaCOMMUNITY
Explore all skills by this author →
Website𝕏@csinvaGitHub

agentic-imodels

Train sklearn-compatible interpretable regressors that emit readable coefficient and rule representations.

14|3|Updated Mar 12, 2026
Search Tags:#explainability#sklearn#tabular-data#interpretable#regressor#LLM-readable#model-outputs
One-click install
npx skills add https://github.com/csinva/agentic-imodels --skill agentic-imodels
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentic-imodels
Source: https://github.com/csinva/agentic-imodels/tree/main/e2e_experiments/blade-evaluation-copilot-gemini/outputs_custom_v2_run3/reading
Command: npx skills add https://github.com/csinva/agentic-imodels --skill agentic-imodels

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The Skill provides interpretable tabular regressors with a sklearn-compatible API, exposing human- and LLM-readable representations of the fitted model.

Core Features & Use Cases

  • Sklearn-compatible regressor library with dynamic, interpretable outputs including linear coefficients, hinge terms, and piecewise tables for each feature.
  • Printable model representations enable explanations to stakeholders and automated evaluation by LLMs.
  • Use cases include model auditing, regulatory communication, and rapid prototyping of explainable ML.

Quick Start

Train an interpretable regressor on your dataset and print the model to reveal a readable equation or rule set.

Frequently Asked Questions about agentic-imodels

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I train interpretable tabular regressors that output readable equations?▼

You can train interpretable tabular regressors that emit human- and LLM-readable representations by fitting a sklearn-compatible model that exposes linear coefficients, hinge terms, and piecewise tables directly in reports.

Can I use sklearn-compatible regressors for model auditing on numeric datasets?▼

Yes, sklearn-compatible regressors support model auditing on small- to mid-sized numeric datasets by generating printable per-feature representations, enabling clear explanations to stakeholders and automated LLM evaluation.

What is the best way to extract human-readable rules from a fitted regression model?▼

The best way to extract human-readable rules is using interpretable regressors that print fitted equations and rule sets, making model outputs directly accessible for regulatory communication and explainable ML prototyping.

Does this interpretable regression approach work with LLMs for automated model evaluation?▼

Yes, interpretable regression works with LLMs by emitting LLM-readable representations of the fitted model, allowing automated evaluation of coefficients, hinge terms, and piecewise tables without manual parsing.

When should I use interpretable tabular regressors instead of standard black-box models?▼

Use interpretable tabular regressors when interpretability of the equation or rules matters, such as for regulatory communication, model auditing, or rapid prototyping on small- to mid-sized numeric datasets where explanations are required.

What format are the model outputs presented in for stakeholder reports?▼

Model outputs are presented as printable per-feature representations, including linear coefficients, hinge terms, and piecewise tables, enabling direct integration into reports for stakeholder explanations.

Related Skills

Explore More Data & Analytics Skills→
SriRamkunamsettySriRamkunamsetty

sparse-autoencoder-training

Train Sparse Autoencoders with SAELens to decompose transformer activations into interpretable features.

Community
Intermediate
48Nauts-Operator48Nauts-Operator
3

attack-tree-construction

Build attack trees with OR/AND/LEAF nodes and threat attributes.

Community
Advanced
ovachieverovachiever
52

pytdc

Provide standardized drug discovery datasets with train/validation/test splits.

Community
Intermediate
AlterLab-IEUAlterLab-IEU
58

alterlab-statsmodels

Configure StatsModels linear, GLM, discrete, and time-series models with diagnostics.

Community
Advanced