# Scikit Learn

> Aprendizaje automático en Python con scikit-learn: clasificación, regresión, clustering, reducción de dimensionalidad, evaluación de modelos, ajuste de hiperparámetros, preprocesamiento y pipelines de ML.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scikit-learn
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scikit-learn.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: BSD-3-Clause license
Actualizado: el mes pasado
Coste de contexto: 95 tok instalada, 2.6k tok al activarse, 30.2k tok con todos los archivos del bundle
Bundle: 11 archivos, 118 KB
Permisos que pide: read write edit bash

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scikit-learn --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scikit-learn --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scikit-learn --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scikit-learn --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scikit-learn --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill scikit-learn --agent cline
```

## Qué hace

- Guía tareas de clasificación, regresión, clustering y reducción de dimensionalidad con scikit-learn
- Construye pipelines completos con Pipeline y ColumnTransformer para datos mixtos numéricos y categóricos
- Evalúa modelos con validación cruzada, búsqueda de hiperparámetros (Grid/RandomizedSearchCV) y métricas
- Incluye scripts de ejemplo para un pipeline de clasificación completo y un análisis de clustering
- Documenta preprocesamiento (escalado, codificación, imputación) y selección de características

## Cuándo usarla

- Construir modelos de clasificación o regresión
- Realizar clustering o reducción de dimensionalidad
- Evaluar el rendimiento de un modelo con validación cruzada o ajustar hiperparámetros
- Crear pipelines de ML para producción o comparar algoritmos

## Qué la activa

- "Entrena un RandomForestClassifier y evalúa con validación cruzada"
- "Crea un pipeline con ColumnTransformer para datos numéricos y categóricos"
- "Compara algoritmos de clustering con el método del codo y silhouette"
- "Ajusta hiperparámetros con GridSearchCV para este modelo"

## Antes de instalar

- Requiere Python 3.11+ y scikit-learn 1.7+, con NumPy y SciPy; matplotlib/seaborn son opcionales para los scripts de ejemplo.

## Archivos

- SKILL.md — 10 KB
- references/common_workflows.md — 3 KB
- references/core_capabilities.md — 5 KB
- references/model_evaluation.md — 15 KB
- references/pipelines_and_composition.md — 14 KB
- references/preprocessing.md — 15 KB
- references/quick_reference.md — 11 KB
- references/supervised_learning.md — 11 KB
- references/unsupervised_learning.md — 15 KB
- scripts/classification_pipeline.py — 8 KB
- scripts/clustering_analysis.py — 11 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo BSD-3-Clause license. Esta sección es el documento original y está en inglés.

# 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

Five capability areas are documented in
[references/core_capabilities.md](references/core_capabilities.md), with per-topic detail
in [references/supervised_learning.md](references/supervised_learning.md),
[references/unsupervised_learning.md](references/unsupervised_learning.md),
[references/model_evaluation.md](references/model_evaluation.md),
[references/preprocessing.md](references/preprocessing.md), and
[references/pipelines_and_composition.md](references/pipelines_and_composition.md):

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.

Two worked workflows are in
[references/common_workflows.md](references/common_workflows.md).

## Example Scripts

### Classification Pipeline

Run a complete classification workflow with preprocessing, model comparison, hyperparameter tuning, and evaluation:

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

This script demonstrates:
- Handling mixed data types (numeric and categorical)
- Model comparison using cross-validation
- Hyperparameter tuning with GridSearchCV
- Comprehensive evaluation with multiple metrics
- Feature importance analysis

### Clustering Analysis

Perform clustering analysis with algorithm comparison and visualization:

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

This script demonstrates:
- Finding optimal number of clusters (elbow method, silhouette analysis)
- Comparing multiple clustering algorithms (K-Means, DBSCAN, Agglomerative, Gaussian Mixture)
- Evaluating clustering quality without ground truth
- Visualizing results with PCA projection

## Reference Documentation

This skill includes comprehensive reference files for deep dives into specific topics:

### Quick Reference
**File:** `references/quick_reference.md`
- Common import patterns and installation instructions
- Quick workflow templates for common tasks
- Algorithm selection cheat sheets
- Common patterns and gotchas
- Performance optimization tips

### Supervised Learning
**File:** `references/supervised_learning.md`
- Linear models (regression and classification)
- Support Vector Machines
- Decision Trees and ensemble methods
- K-Nearest Neighbors, Naive Bayes, Neural Networks
- Algorithm selection guide

### Unsupervised Learning
**File:** `references/unsupervised_learning.md`
- All clustering algorithms with parameters and use cases
- Dimensionality reduction techniques
- Outlier and novelty detection
- Gaussian Mixture Models
- Method selection guide

### Model Evaluation
**File:** `references/model_evaluation.md`
- Cross-validation strategies
- Hyperparameter tuning methods
- Classification, regression, and clustering metrics
- Learning and validation curves
- Best practices for model selection

### Preprocessing
**File:** `references/preprocessing.md`
- Feature scaling and normalization
- Encoding categorical variables
- Missing value imputation
- Feature engineering techniques
- Custom transformers

### Pipelines and Composition
**File:** `references/pipelines_and_composition.md`
- Pipeline construction and usage
- ColumnTransformer for mixed data types
- FeatureUnion for parallel transformations
- Complete end-to-end examples
- Best practices

## Best Practices

### Always Use Pipelines
Pipelines prevent data leakage and ensure consistency:
```python
# Good: Preprocessing in pipeline
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('model', LogisticRegression())
])

# Bad: Preprocessing outside (can leak information)
X_scaled = StandardScaler().fit_transform(X)
```

### Fit on Training Data Only
Never fit on test data:
```python
# Good
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)  # Only transform

# Bad
scaler = StandardScaler()
X_all_scaled = scaler.fit_transform(np.vstack([X_train, X_test]))
```

### Use Stratified Splitting for Classification
Preserve class distribution:
```python
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, stratify=y, random_state=42
)
```

### Set Random State for Reproducibility
```python
model = RandomForestClassifier(n_estimators=100, random_state=42)
```

### Choose Appropriate Metrics
- Balanced data: Accuracy, F1-score
- Imbalanced data: Precision, Recall, ROC AUC, Balanced Accuracy
- Cost-sensitive: Define custom scorer

### Scale Features When Required
Algorithms requiring feature scaling:
- SVM, KNN, Neural Networks
- PCA, Linear/Logistic Regression with regularization
- K-Means clustering

Algorithms not requiring scaling:
- Tree-based models (Decision Trees, Random Forest, Gradient Boosting)
- Naive Bayes

## Troubleshooting Common Issues

### ConvergenceWarning
**Issue:** Model didn't converge
**Solution:** Increase `max_iter` or scale features
```python
model = LogisticRegression(max_iter=1000)
```

### Poor Performance on Test Set
**Issue:** Overfitting
**Solution:** Use regularization, cross-validation, or simpler model
```python
# Add regularization
model = Ridge(alpha=1.0)

# Use cross-validation
scores = cross_val_score(model, X, y, cv=5)
```

### Memory Error with Large Datasets
**Solution:** Use algorithms designed for large data
```python
# Use SGD for large datasets
from sklearn.linear_model import SGDClassifier
model = SGDClassifier()

# Or MiniBatchKMeans for clustering
from sklearn.cluster import MiniBatchKMeans
model = MiniBatchKMeans(n_clusters=8, batch_size=100)
```

## Additional Resources

- Official Documentation: https://scikit-learn.org/stable/
- User Guide: https://scikit-learn.org/stable/user_guide.html
- API Reference: https://scikit-learn.org/stable/api/index.html
- Examples Gallery: https://scikit-learn.org/stable/auto_examples/index.html

## Dónde encaja

- Categoría: [Datos y analítica](https://skillsagentes.com/categorias/datos-analitica.md) — Consulta, limpia y visualiza datos sin salir del agente.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas.
- [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual.
- [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver).
- [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo.
- [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras.

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