traincli 0.1.1

Train ML models from a single command — no code required

TrainCLI

Train ML models from a single command — no code required.

TrainCLI is a command-line tool that automates the entire machine learning pipeline: data profiling, cleaning, model selection, training, and export. Perfect for rapid prototyping, baseline models, or learning ML workflows.

Features

• 🚀 One-command training — train --data data.csv --target column
• 🤖 Auto model selection — Cross-validation picks the best algorithm
• 🧹 Smart data cleaning — Handles missing values, outliers, encoding
• 📊 Data profiling — Detects issues before training
• 🔍 Leakage detection — Warns about suspiciously correlated features
• 🧠 LLM-powered insights — Optional AI explanations (watsonx.ai)
• 📦 Export everything — model.pkl, train.py, report.md, metrics.json
• 🔧 Reproducible — Generated train.py is standalone

Installation

pip install traincli

Quick Start

1. Initialize (first time only)

train init

This sets up your API keys for LLM features (optional — works offline too).

2. Train a model

train --data sales.csv --target revenue

That's it! TrainCLI will:

• Profile your data
• Clean it automatically (or ask for input)
• Detect if it's classification or regression
• Try multiple models via cross-validation
• Pick the best one
• Export everything to ./output/

3. Use your model

import pickle
import pandas as pd

with open("output/model.pkl", "rb") as f:
    model = pickle.load(f)

new_data = pd.DataFrame([{"feature1": 10, "feature2": "A"}])
prediction = model.predict(new_data)

Or run the generated train.py to retrain from scratch.

Commands

Command	Description
train --data <file> --target <col>	Train a model on a CSV file
train --dir <folder> --target <col>	Train on all CSVs in a folder
train --model <name>	Use a specific model (e.g., random_forest)
train --model ?	Pick model interactively
train --task classification	Override problem type detection
train --drop col1,col2	Drop columns (e.g., to fix leakage)
train --preview	Profile data without training
train --auto	Skip all prompts (for CI/CD)
train init	Configure API keys

Examples

Basic training

train --data customers.csv --target churn

Force a specific model

train --data sales.csv --target price --model gradient_boosting

Interactive model selection

train --data data.csv --target label --model ?

Drop leaky columns

train --data jobs.csv --target salary --drop min_salary,max_salary

Batch processing

train --dir ./datasets/ --target outcome --auto

Preview mode

train --data data.csv --target col --preview

Output Files

After training, you'll get:

• model.pkl — Trained scikit-learn pipeline (ready to use)
• train.py — Standalone script to retrain the model
• report.md — Human-readable summary with metrics
• metrics.json — Machine-readable metrics
• session.json — Full training session metadata

Supported Models

TrainCLI includes:

Classification:

• Logistic Regression
• Random Forest
• Gradient Boosting (XGBoost)
• Support Vector Machine
• K-Nearest Neighbors

Regression:

• Linear Regression
• Ridge Regression
• Random Forest
• Gradient Boosting (XGBoost)
• Support Vector Regression

LLM Features (Optional)

TrainCLI can use IBM watsonx.ai to:

• Explain model results in plain English
• Suggest improvements
• Generate educational comments in train.py

To enable:

train init
# Choose "watsonx" and enter your API key

Works offline if no API key is configured.

Data Leakage Detection

TrainCLI automatically checks for:

• Features with >0.98 correlation to target
• Column names containing the target name
• Linear transformations of the target

Example warning:

⚠ Leakage suspected:
  → min_salary: correlation with target is 0.999 — suspiciously high
  
  Suggested fix: train --drop min_salary ...

Requirements

• Python 3.9+
• pandas, scikit-learn, xgboost
• Optional: ibm-watsonx-ai (for LLM features)

Development

git clone https://github.com/yourusername/traincli
cd traincli
pip install -e .

License

MIT

Contributing

Contributions welcome! Open an issue or PR.

MCP Server (Claude Code Integration)

TrainCLI includes an MCP server that lets Claude Code train models directly:

Setup

1. Install TrainCLI:

pip install traincli

2. Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on Mac):

{
  "mcpServers": {
    "traincli": {
      "command": "traincli-mcp"
    }
  }
}

3. Restart Claude Desktop

Available Tools

• train_model — Train a model on a CSV file
• profile_dataset — Profile data without training
• suggest_target — Suggest the best target column

Example Usage in Claude

User: Train a model on sales.csv to predict revenue

Claude: [calls train_model tool]
✓ Model trained: Gradient Boosting
✓ R² = 0.847
✓ Files saved to ./output/

Roadmap

• Meta-model for smarter algorithm selection
• More preprocessing options
• Feature engineering suggestions
• Hyperparameter tuning

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Made with ❤️ for developers who want ML without the boilerplate.