datarobot-model-training

Automate DataRobot model training from project setup to model selection.

24|22|Updated Dec 14, 2025

Search Tags:#feature engineering #model training #model selection #python sdk #automl #datarobot #time series forecasting

Ready to run

npx skills add https://github.com/datarobot-oss/datarobot-agent-skills --skill datarobot-model-training

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: datarobot-model-training
Source: https://github.com/datarobot-oss/datarobot-agent-skills/tree/main/skills/datarobot-model-training
Command: npx skills add https://github.com/datarobot-oss/datarobot-agent-skills --skill datarobot-model-training

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires datarobot, and includes scripts (resource) components.

What problem does it solve?

DataRobot model training often requires coordinating many steps—project setup, AutoML configuration, feature engineering review, and model selection—so teams need a single, reliable workflow to execute consistently.

Core Features & Use Cases

Project creation & dataset upload guidance: Covers creating projects from datasets and setting targets, including common partitioning needs.
AutoML training configuration & execution: Guides selecting training modes (Quick/Comprehensive/Manual) and starting training while monitoring status.
Model analysis & selection: Helps compare models and interpret key metrics and feature impact to choose a deployment-ready model.
SDK + runnable scripts: Provides DataRobot Python SDK patterns and utility scripts for creating projects, starting training, and listing models.

Quick Start

Ask your agent: "Create a DataRobot project from dataset_123, set revenue as the target, start Quick AutoML training, and then list the top models by AUC for project_456."

Frequently Asked Questions about datarobot-model-training

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

FAQPage Schema

How do I automate DataRobot model training and selection end-to-end?▼

Automate DataRobot model training by coordinating project setup, target configuration, AutoML execution, and model comparison. This handles classification, regression, and time-series forecasting workflows using the DataRobot Python SDK and helper scripts to create projects, start training, and list models.

How do I start AutoML training in DataRobot for a new dataset?▼

Start AutoML training by creating a project from your dataset, setting the target variable, and selecting a training mode like Quick or Comprehensive. The provided helper scripts and DataRobot Python SDK patterns guide you through configuring partitioning, executing training, and monitoring job status.

Can I use the DataRobot Python SDK for time series forecasting?▼

The DataRobot Python SDK supports time series forecasting alongside classification and regression tasks. You apply optional time-series settings within the project context to configure training options, monitor status, and select the best-performing forecasting models.

What's the best way to compare models and select the best one in DataRobot?▼

Compare models by interpreting key metrics and feature impact to choose a deployment-ready model. The workflow guides you through listing trained models and analyzing performance to identify the top-performing model for your specific classification or regression scenario.

Do I need a DataRobot project context to train models with the Python SDK?▼

You need an existing DataRobot project context before configuring training options and starting AutoML execution. The workflow requires this project context to set targets, apply optional time-series settings, and monitor training status to select the best-performing models.

Does DataRobot AutoML support both Quick and Comprehensive training modes?▼

DataRobot AutoML supports Quick, Comprehensive, and Manual training modes. You select the appropriate mode during configuration to start training, monitor status, and evaluate models based on key metrics and feature impact for your deployment-ready model selection.