domains / data / scikit-learn-ml-pipelines / scikit-learn

scikit-learn

Skill card

combined

The K-Dense-AI copy is the most complete and current, but the other copies contain material constraints and steps it lacks. The foryourhealth111-pixel copy adds a critical scope boundary (excluding causal identification) and a specific use case (ordinary regression) that the K-Dense copy omits. The sickn33 copy adds a mandatory pre-execution step to read a detailed guide and specific script execution commands. The zLanqing copy is a truncated version of the K-Dense content. Because the K-Dense copy lacks the causal analysis exclusion and the sickn33 copy lacks the detailed algorithmic reference, crowning one would hide real alternatives and constraints.

Repositories

Other copies

Combined skill

---
name: scikit-learn
description: Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.
license: BSD-3-Clause license
metadata:
  skill-author: K-Dense Inc.
  version: "1.5"
---

# Scikit-learn

## Overview

This skill provides comprehensive guidance for machine learning tasks using scikit-learn. Use this skill for classification, regression, clustering, dimensionality reduction, preprocessing, model evaluation, and building production-ready ML pipelines.

## Scope Boundaries

- **Use for:** Ordinary regression analysis (fitting, coefficients, confidence intervals, residual checks) where the need is not causal identification.
- **Do NOT use for:** Causal identification, policy-effect claims, DiD, synthetic control, or interrupted time-series effect estimation. Use `performing-causal-analysis` for those tasks.

## Installation

Targets **scikit-learn 1.9.1**, verified with Python 3.13. The release requires Python 3.11+.

```bash
# Install scikit-learn using uv
uv pip install "scikit-learn==1.9.1"

# Optional: plotting utilities and bundled script dependencies
uv pip install "scikit-learn[plots]==1.9.1" matplotlib pandas
```

## Pre-Execution Requirements

Before executing complex workflows, read the relevant reference documentation. For end-to-end work, consult the detailed guide if available in the skill directory.

## Quick Start

### Classification Example

```python
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)

# Preprocess
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train_scaled, y_train)

# Evaluate
y_pred = model.predict(X_test_scaled)
print(classification_report(y_test, y_pred))
```

### Complete Pipeline with Mixed Data

```python
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.ensemble import GradientBoostingClassifier

# Define feature types
numeric_features = ['age', 'income']
categorical_features = ['gender', 'occupation']

# Create preprocessing pipelines
numeric_transformer = Pipeline([
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
])

categorical_transformer = Pipeline([
    ('imputer', SimpleImputer(strategy='most_frequent')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
])

# Combine transformers
preprocessor = ColumnTransformer([
    ('num', numeric_transformer, numeric_features),
    ('cat', categorical_transformer, categorical_features)
])

# Full pipeline
model = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', GradientBoostingClassifier(random_state=42))
])

# Fit and predict
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
```

## Core Capabilities

1. **Supervised learning** — classification and regression estimator families.
2. **Unsupervised learning** — clustering, decomposition, and manifold learning.
3. **Model evaluation and selection** — metrics, cross-validation, and hyperparameter search.
4. **Data preprocessing** — scaling, encoding, imputation, and feature selection.
5. **Pipelines and composition** — `Pipeline` and `ColumnTransformer`.

Always fit preprocessing inside a `Pipeline` so it is refit per cross-validation fold; scaling or imputing before splitting leaks test information into training.

## Example Scripts

If bundled scripts are available in the skill directory:

### Classification Pipeline

```bash
python scripts/classification_pipeline.py
```

### Clustering Analysis

```bash
python scripts/clustering_analysis.py
```

## Limitations

- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.