# Scikit Learn

> Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.

- Skill: `ranbot-ai/scikit-learn` (Agent Skill)
- Install (CLI): `npx skillmds add ranbot-ai/scikit-learn`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ranbot-ai/scikit-learn/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: ranbot-ai (https://skillmd.com/u/ranbot-ai)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ranbot-ai/scikit-learn

---



# 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

```bash
# Install scikit-learn using uv
uv uv pip install scikit-learn

# Optional: Install visualization dependencies
uv uv pip install matplotlib seaborn

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

## 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.

**Key algorithms:**
- **Linear models**: Logistic Regression, Linear Regression, Ridge, Lasso, ElasticNet
- **Tree-based**: Decision Trees, Random Forest, Gradient Boosting
- **Support Vector Machines**: SVC, SVR with various kernels
- **Ensemble methods**: AdaBoost, Voting, Stacking
- **Neural Networks**: MLPClassifier, MLPRegressor
- **Others**: Naive Bayes, K-Nearest Neighbors

**When to use:**
- Classification: Predicting discrete categories (spam detection, image classification, fraud detection)
- Regression: Predicting continuous values (price prediction, demand forecasting)

**See:** `references/supervised_learning.md` for detailed algorithm documentation, parameters, and usage examples.

### 2. Unsupervised Learning

Discover patterns in unlabeled data through clustering and dimensionality reduction.

**Clustering algorithms:**
- **Partition-based**: K-Means, MiniBatchKMeans
- **Density-based**: DBSCAN, HDBSCAN, OPTICS
- **Hierarchical**: AgglomerativeClustering
- **Probabilistic**: Gaussian Mixture Models
- **Others**: MeanShift, SpectralClustering, BIRCH

**Dimensionality reduction:**
- **Linear**: PCA, TruncatedSVD, NMF
- **Manifold learning**: t-SNE, UMAP, Isomap, LLE
- **Feature extraction**: FastICA, LatentDirichletAllocation

**When to use:**
- Customer segmentation, anomaly detection, data visualization
- Reducing feature dimensions, exploratory data analysis
- Topic modeling, image compression

**See:** `references/unsupervised_learning.md` for detailed documentation.

### 3. Model Evaluation and Selection

Tools for robust model evaluation, cross-validation, and hyperparameter tuning.

**Cross-validation strategies:**
- KFold, StratifiedKFold (classification)
- TimeSeriesSplit (temporal data)
- GroupKFold (grouped samples)

**Hyperparameter tuning:**
- GridSearchCV (exhaustive search)
- RandomizedSearchCV (random sampling)
- HalvingGridSearchCV (successive halving)

**Metrics:**
- **Cla