# lightgbm

A Cursor rule enforcing best practices for LightGBM in Python ML pipelines: Scikit-learn API usage, reproducibility flags, and native categorical handling.

- rule · `type=rule` · Cursor · ★ 3580
- Author: sanjeed5
- License: CC0-1.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/lightgbm.mdc
- Updated: 2026-05-19
- HTML: https://affpapa.org/en/nevibecode/rules/lightgbm

## What it does

The rule triggers on `**/*.py` files, directing Cursor to use `LGBMClassifier` and `LGBMRegressor` from the Scikit-learn API. It replaces hardcoded inline models with dedicated builder functions that accept typed parameter dictionaries or external YAML configs. To avoid non-deterministic runs, it requires setting `random_state` and `deterministic=True`.

For tabular data, the instructions instruct Cursor to use native handling with `categorical_feature` rather than manual one-hot encoding. During hyperparameter optimization with Optuna or FLAML, it enforces the condition `num_leaves < 2^max_depth`. If `num_leaves` exceeds that bound, leaf-wise tree growth causes overfitting. Training code must also include validation sets paired with `callbacks=[lgb.early_stopping(...)]`.

## Install

### Cursor (native platform)

**Manually: put the files in place**
- File: `.cursor/rules/lightgbm.mdc` — Frontmatter: description, globs, alwaysApply

### Claude Code

**Manually: put the files in place**
- File: `.claude/rules/lightgbm.md` — The paths frontmatter field limits the files: move globs from the source into paths

### Codex

**AGENTS.md**
- Codex reads AGENTS.md: paste the rule text there

### GitHub Copilot

**Manually: put the files in place**
- File: `.github/instructions/lightgbm.instructions.md` — The applyTo field sets which files it applies to: move globs from the source into applyTo

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/rules/lightgbm.md`

### Cline

**Manually: put the files in place**
- File: `.clinerules/lightgbm.md`

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/sanjeed5/awesome-cursor-rules-mdc/tree/HEAD/rules-mdc/lightgbm.mdc

## When to use it
- Enforce builder functions for `LGBMClassifier` with external configs
- Enable deterministic training with explicit seed controls
- Pass raw categorical columns using the native `categorical_feature` parameter
- Prevent overfitting by constraining `num_leaves` relative to `max_depth`

## Example prompts
- Write a `build_lgbm_classifier` function taking hyperparameters from a YAML config
- Create a LightGBM training script using early stopping callbacks and a validation split
- Set up an Optuna objective function for tuning LightGBM with proper leaf-to-depth constraints

## Source text · lightgbm.mdc · CC0-1.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 1b880757409974f1.

BEGIN THIRD-PARTY FILE 1b880757409974f1
````text
---
description: This guide provides opinionated, actionable best practices for using LightGBM in production-grade Python ML pipelines, focusing on performance, reproducibility, and maintainability.
globs: **/*.py
---
# lightgbm Best Practices

LightGBM is our go-to for high-performance tabular modeling. These rules ensure our LightGBM implementations are fast, reliable, and maintainable.

## 1. Code Structure & Reproducibility

Always encapsulate model creation and ensure runs are repeatable.

### 1.1. Use Scikit-learn API & Model Builder Functions

Always use `LGBMClassifier` or `LGBMRegressor` for `sklearn` compatibility. Wrap model instantiation in a function for clean, version-controlled hyperparameter management.

❌ **BAD: Inline model creation with hardcoded parameters**
```python
# my_script.py
import lightgbm as lgb
model = lgb.LGBMClassifier(n_estimators=100, learning_rate=0.1, max_depth=7)
model.fit(X_train, y_train)
```

✅ **GOOD: Function-based model creation with type hints and external config**
```python
# models/lgbm_model.py
import lightgbm as lgb
from typing import Dict, Any
import yaml # Or use a dataclass for params

def build_lgbm_classifier(params: Dict[str, Any]) -> lgb.LGBMClassifier:
    """Builds a LightGBM Classifier with specified hyperparameters."""
    return lgb.LGBMClassifier(**params)

# config/lgbm_params.yaml
# classifier_v1:
#   objective: binary
#   n_estimators: 500
#   learning_rate: 0.05
#   num_leaves: 31
#   max_depth: 6
#   random_state: 42
#   n_jobs: -1
#   colsample_bytree: 0.8
#   subsample: 0.8
#   reg_alpha: 0.1
#   reg_lambda: 0.1

# my_script.py
import yaml
from models.lgbm_model import build_lgbm_classifier

with open("config/lgbm_params.yaml", "r") as f:
    params = yaml.safe_load(f)['classifier_v1']

model = build_lgbm_classifier(params)
model.fit(X_train, y_train)
```

### 1.2. Set Reproducibility Flags

Ensure `random_state` (or `seed`) and `deterministic=True` are always set for consistent results.

❌ **BAD: Non-deterministic training**
```python
model = lgb.LGBMClassifier(n_estimators=100) # Results vary on each run
```

✅ **GOOD: Fully reproducible training**
```python
model = lgb.LGBMClassifier(n_estimators=100, random_state=42, deterministic=True)
```

## 2. Data Handling

Leverage LightGBM's native capabilities for efficiency.

### 2.1. Native Categorical Feature Handling

Feed raw categorical columns directly. LightGBM handles them efficiently, saving memory and speeding up training.

❌ **BAD: Manual one-hot or label encoding**
```python
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder(handle_unknown='ignore', sparse_output=False)
X_train_encoded = encoder.fit_transform(X_train[categorical_cols])
# Then concatenate with numerical features
model.fit(X_train_encoded, y_train)
```

✅ **GOOD: Use `categorical_feature` parameter**
```python
categorical_feature_names = ['feature_A', 'feature_B']
categorical_feature_indices = [X_train.columns.get_loc(col) for col in categorical_feature_names]

model = lgb.LGBMClassifier(categorical_feature=categorical_feature_indices, **params)
model.fit(X_train, y_train)
```

### 2.2. Missing Values

LightGBM handles missing values natively. While it can split on them, consider imputing sensible defaults (e.g., mean, target encoding) for improved stability in some cases.

✅ **GOOD: Let LightGBM handle, or impute strategically**
```python
# Option 1: Let LightGBM handle (default behavior)
model = lgb.LGBMClassifier(**params)
model.fit(X_train_with_nans, y_train)

# Option 2: Impute for potential stability/performance gains
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy='mean')
X_train_imputed = imputer.fit_transform(X_train_with_nans)
model.fit(X_train_imputed, y_train)
```

## 3. Hyperparameter Tuning & Training
````
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