scikit-learn

Files (11)

• LICENSE
• SKILL.md
• local.patch
• model_evaluation.md · references/
• pipelines_and_composition.md · references/
• preprocessing.md · references/
• quick_reference.md · references/
• supervised_learning.md · references/
• unsupervised_learning.md · references/
• classification_pipeline.py · scripts/
• clustering_analysis.py · scripts/

SKILL.md (first lines)

# Scikit-learn

## Overview

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

## Installation

Tested against **scikit-learn 1.8.0** (stable; December 2025). Requires **Python 3.11–3.14** (free-threaded CPython 3.14 wheels available in 1.8+).

Install the PyPI package **`scikit-learn`** (not the deprecated `sklearn` package on PyPI). Import in code as `sklearn`.

```bash
# Install scikit-learn using uv
uv pip install "scikit-learn>=1.7"

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

# Commonly used with
uv pip install pandas numpy
```

Check your version:

```python
import sklearn
print(sklearn.__version__)
```

## When to Use This Skill

Use the scikit-learn skill when:

- Building classification or regression models
- Performing clustering or dimensionality reduction
- Preprocessing and transforming data for machine learning
- Evaluating model performance with cross-validation
- Tuning hyperparameters with grid or random search
- Creating ML pipelines for production workflows
- Comparing different algorithms for a task
- Working with both structured (tabular) and text data
- Need interpretable, classical machine learning approaches

## 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

Comprehensive algorithms for classification and regression tasks.

From Kilo-Org/kilo-marketplace, Apache-2.0.

More from Kilo-Org/kilo-marketplace