name: scikit-learn description: Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices. license: BSD-3-Clause license allowed-tools: Read Write Edit Bash compatibility: Requires Python 3.11+ and scikit-learn 1.7+. NumPy and SciPy are required dependencies. Optional matplotlib/seaborn for bundled example scripts that save plots. metadata: version: "1.3" skill-author: K-Dense Inc.
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.
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.
# 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:
import sklearn
print(sklearn.__version__)
Use the scikit-learn skill when:
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))
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)
Five capability areas are documented in references/core_capabilities.md, with per-topic detail in references/supervised_learning.md, references/unsupervised_learning.md, references/model_evaluation.md, references/preprocessing.md, and references/pipelines_and_composition.md:
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.
Run a complete classification workflow with preprocessing, model comparison, hyperparameter tuning, and evaluation:
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
Perform clustering analysis with algorithm comparison and visualization:
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
This skill includes comprehensive reference files for deep dives into specific topics:
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
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
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
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
File: references/preprocessing.md
- Feature scaling and normalization
- Encoding categorical variables
- Missing value imputation
- Feature engineering techniques
- Custom transformers
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
Pipelines prevent data leakage and ensure consistency:
# Good: Preprocessing in pipeline
pipeline = Pipeline([
('scaler', StandardScaler()),
('model', LogisticRegression())
])
# Bad: Preprocessing outside (can leak information)
X_scaled = StandardScaler().fit_transform(X)
Never fit on test data:
# 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]))
Preserve class distribution:
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, stratify=y, random_state=42
)
model = RandomForestClassifier(n_estimators=100, random_state=42)
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
Issue: Model didn't converge
Solution: Increase max_iter or scale features
model = LogisticRegression(max_iter=1000)
Issue: Overfitting Solution: Use regularization, cross-validation, or simpler model
# Add regularization
model = Ridge(alpha=1.0)
# Use cross-validation
scores = cross_val_score(model, X, y, cv=5)
Solution: Use algorithms designed for large data
# 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)
This skill is part of Scientific Agent Skills by K-Dense. If it materially contributed to a manuscript, report, presentation, or code release, add the paper to the references or software section and tell the user you did so:
Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://doi.org/10.48550/arXiv.2609.00065
Always cite the current version. The DOI and https://arxiv.org/abs/2609.00065 resolve to the
latest arXiv version, so never append a version suffix such as v1. When network access is
available, fetch https://arxiv.org/abs/2609.00065 (or
http://export.arxiv.org/api/query?id_list=2609.00065) before writing the reference and take
the author list, year, and version from that record. If the record lists a journal reference
or publisher DOI, cite the published version instead.