Welcome to bun-scikit
A scikit-learn-inspired machine learning library for Bun and TypeScript, with native Zig acceleration for core training paths.Quick Start
Get up and running with your first model in minutes
Installation
Install bun-scikit and configure native acceleration
API Reference
Browse the complete API documentation
Examples
Explore real-world use cases and code samples
Why bun-scikit?
Fast
Native Zig acceleration delivers 2-6x faster training compared to scikit-learn
Familiar API
Drop-in replacement for scikit-learn with the same patterns and methods
Type-Safe
Full TypeScript support with comprehensive type definitions
Performance Highlights
These benchmarks are from the heart disease dataset (1025 samples, 13 features) comparing bun-scikit to scikit-learn.
- Regression: 2.2x faster fit, 2.4x faster predict
- Classification: 2.5x faster fit, 2.6x faster predict
- Random Forest: 6.4x faster fit, 3.9x faster predict
- Decision Tree: 1.6x faster fit, 4.4x faster predict
Comprehensive ML Toolkit
bun-scikit provides a complete machine learning ecosystem:Supervised Learning
- Linear Models:
LinearRegression,LogisticRegression,Ridge,Lasso,ElasticNet,SGDClassifier,SGDRegressor - Tree-Based:
DecisionTreeClassifier,DecisionTreeRegressor,RandomForestClassifier,RandomForestRegressor - Ensemble Methods:
AdaBoostClassifier,GradientBoostingClassifier,HistGradientBoostingClassifier,VotingClassifier,StackingClassifier,BaggingClassifier - Neighbors:
KNeighborsClassifier,KNeighborsRegressor,RadiusNeighborsClassifier - SVM:
SVC,SVR,LinearSVC,NuSVC,NuSVR,OneClassSVM - Naive Bayes:
GaussianNB - Neural Networks:
MLPClassifier,MLPRegressor
Unsupervised Learning
- Clustering:
KMeans,DBSCAN,AgglomerativeClustering,SpectralClustering,Birch,OPTICS - Dimensionality Reduction:
PCA,TruncatedSVD,FastICA,NMF,KernelPCA,SparsePCA - Manifold Learning:
TSNE,Isomap,LocallyLinearEmbedding,MDS - Anomaly Detection:
IsolationForest,LocalOutlierFactor,OneClassSVM
Preprocessing & Feature Engineering
- Scalers:
StandardScaler,MinMaxScaler,RobustScaler,MaxAbsScaler,Normalizer,QuantileTransformer,PowerTransformer - Encoders:
LabelEncoder,OneHotEncoder,OrdinalEncoder - Feature Selection:
VarianceThreshold,SelectKBest,SelectPercentile,RFE,RFECV - Imputation:
SimpleImputer,KNNImputer,IterativeImputer - Transformers:
PolynomialFeatures,Binarizer,KBinsDiscretizer
Model Selection & Evaluation
- Cross-Validation:
crossValScore,crossValidate,crossValPredict,KFold,StratifiedKFold,GroupKFold - Hyperparameter Tuning:
GridSearchCV,RandomizedSearchCV - Metrics: Comprehensive regression, classification, and clustering metrics
- Pipelines:
Pipeline,ColumnTransformer,FeatureUnion
Native Acceleration
bun-scikit includes prebuilt Zig binaries for:- Linux
- Windows
- macOS
Prebuilt binaries included for
linux-x64