# Aleberriz Agent Skills Analytics Ml > ML Modeling - Skill: `tomevault-io/aleberriz-agent-skills-analytics-ml` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/aleberriz-agent-skills-analytics-ml` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/aleberriz-agent-skills-analytics-ml/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/aleberriz-agent-skills-analytics-ml --- # ML Modeling *This skill is planned but not yet implemented.* Conventions for applied machine learning in analytics contexts: predicting outcomes, scoring users, forecasting metrics. This is the prescriptive/predictive domain, distinct from experimentation (which establishes causation through randomization). When complete, this skill will cover: - Feature engineering patterns for tabular business data - Model evaluation: choosing the right metric for the business question (precision vs. recall, RMSE vs. MAE, AUC vs. F1) - Validation strategy: time-based splits for temporal data, stratification, cross-validation - Overfitting discipline: regularization, feature selection, holdout discipline - Forecasting conventions: time series decomposition, seasonality, trend - Deployment considerations: batch vs. real-time, monitoring, drift detection ## References - [Anthropic Agent Skills](https://github.com/anthropics/skills): check for updated patterns and templates - [scikit-learn](https://scikit-learn.org/): the default ML library for tabular analytics work - [statsmodels](https://www.statsmodels.org/): statistical modeling in Python --- > Source: [aleberriz/agent-skills](https://github.com/aleberriz/agent-skills) — distributed by [TomeVault](https://tomevault.io).