# bun-scikit ## Docs - [AgglomerativeClustering](https://mintlify.wiki/Seyamalam/bun-scikit/api/clustering/agglomerative-clustering.md): Hierarchical clustering using bottom-up approach - [Birch](https://mintlify.wiki/Seyamalam/bun-scikit/api/clustering/birch.md): Balanced Iterative Reducing and Clustering using Hierarchies - [DBSCAN](https://mintlify.wiki/Seyamalam/bun-scikit/api/clustering/dbscan.md): Density-Based Spatial Clustering of Applications with Noise - [KMeans](https://mintlify.wiki/Seyamalam/bun-scikit/api/clustering/kmeans.md): K-Means clustering algorithm for partitioning data into k clusters - [OPTICS](https://mintlify.wiki/Seyamalam/bun-scikit/api/clustering/optics.md): Ordering Points To Identify the Clustering Structure - [SpectralClustering](https://mintlify.wiki/Seyamalam/bun-scikit/api/clustering/spectral-clustering.md): Clustering using spectral decomposition of affinity matrix - [FastICA](https://mintlify.wiki/Seyamalam/bun-scikit/api/decomposition/fast-ica.md): Independent Component Analysis using the FastICA algorithm - [KernelPCA](https://mintlify.wiki/Seyamalam/bun-scikit/api/decomposition/kernel-pca.md): Non-linear dimensionality reduction using kernel methods - [NMF](https://mintlify.wiki/Seyamalam/bun-scikit/api/decomposition/nmf.md): Non-negative Matrix Factorization for parts-based representation - [PCA](https://mintlify.wiki/Seyamalam/bun-scikit/api/decomposition/pca.md): Principal Component Analysis for dimensionality reduction - [TruncatedSVD](https://mintlify.wiki/Seyamalam/bun-scikit/api/decomposition/truncated-svd.md): Dimensionality reduction using truncated Singular Value Decomposition - [RFE](https://mintlify.wiki/Seyamalam/bun-scikit/api/feature-selection/rfe.md): Recursive Feature Elimination - [SelectKBest](https://mintlify.wiki/Seyamalam/bun-scikit/api/feature-selection/select-k-best.md): Select top K features based on univariate statistical tests - [VarianceThreshold](https://mintlify.wiki/Seyamalam/bun-scikit/api/feature-selection/variance-threshold.md): Remove low-variance features - [ElasticNet](https://mintlify.wiki/Seyamalam/bun-scikit/api/linear-models/elastic-net.md): Elastic Net regression combining L1 and L2 regularization - [Lasso](https://mintlify.wiki/Seyamalam/bun-scikit/api/linear-models/lasso.md): Lasso regression with L1 regularization - [LinearRegression](https://mintlify.wiki/Seyamalam/bun-scikit/api/linear-models/linear-regression.md): Ordinary least squares linear regression - [LogisticRegression](https://mintlify.wiki/Seyamalam/bun-scikit/api/linear-models/logistic-regression.md): Logistic regression classifier with gradient descent or L-BFGS optimization - [Ridge](https://mintlify.wiki/Seyamalam/bun-scikit/api/linear-models/ridge.md): Ridge regression with L2 regularization - [SGD (Stochastic Gradient Descent)](https://mintlify.wiki/Seyamalam/bun-scikit/api/linear-models/sgd.md): Linear models trained using stochastic gradient descent - [Isomap](https://mintlify.wiki/Seyamalam/bun-scikit/api/manifold/isomap.md): Isometric Mapping for non-linear dimensionality reduction - [LocallyLinearEmbedding](https://mintlify.wiki/Seyamalam/bun-scikit/api/manifold/lle.md): Locally Linear Embedding for manifold learning - [MDS](https://mintlify.wiki/Seyamalam/bun-scikit/api/manifold/mds.md): Multidimensional Scaling for distance-preserving dimensionality reduction - [TSNE](https://mintlify.wiki/Seyamalam/bun-scikit/api/manifold/tsne.md): t-Distributed Stochastic Neighbor Embedding for visualization - [BaggingClassifier](https://mintlify.wiki/Seyamalam/bun-scikit/api/meta/bagging-classifier.md): Ensemble classifier using bootstrap aggregating - [CalibratedClassifierCV](https://mintlify.wiki/Seyamalam/bun-scikit/api/meta/calibrated-classifier-cv.md): Probability calibration with cross-validation - [StackingClassifier](https://mintlify.wiki/Seyamalam/bun-scikit/api/meta/stacking-classifier.md): Ensemble classifier using stacked generalization - [VotingClassifier](https://mintlify.wiki/Seyamalam/bun-scikit/api/meta/voting-classifier.md): Ensemble classifier using majority voting - [Classification Metrics](https://mintlify.wiki/Seyamalam/bun-scikit/api/metrics/classification.md): Metrics for evaluating classification models - [Clustering Metrics](https://mintlify.wiki/Seyamalam/bun-scikit/api/metrics/clustering.md): Metrics for evaluating clustering algorithms - [Regression Metrics](https://mintlify.wiki/Seyamalam/bun-scikit/api/metrics/regression.md): Metrics for evaluating regression models - [crossValScore](https://mintlify.wiki/Seyamalam/bun-scikit/api/model-selection/cross-val-score.md): Evaluate a score by cross-validation - [GridSearchCV](https://mintlify.wiki/Seyamalam/bun-scikit/api/model-selection/grid-search-cv.md): Exhaustive search over specified parameter values with cross-validation - [KFold](https://mintlify.wiki/Seyamalam/bun-scikit/api/model-selection/kfold.md): K-Folds cross-validator for splitting data into k consecutive folds - [RandomizedSearchCV](https://mintlify.wiki/Seyamalam/bun-scikit/api/model-selection/randomized-search-cv.md): Randomized search over hyperparameters with cross-validation - [StratifiedKFold](https://mintlify.wiki/Seyamalam/bun-scikit/api/model-selection/stratified-kfold.md): Stratified K-Folds cross-validator that preserves class distribution - [trainTestSplit](https://mintlify.wiki/Seyamalam/bun-scikit/api/model-selection/train-test-split.md): Split arrays into random train and test subsets - [GaussianNB](https://mintlify.wiki/Seyamalam/bun-scikit/api/naive-bayes/gaussian-nb.md): Gaussian Naive Bayes classifier - [KNeighborsClassifier](https://mintlify.wiki/Seyamalam/bun-scikit/api/neighbors/kneighbors-classifier.md): K-Nearest Neighbors classifier for classification tasks - [KNeighborsRegressor](https://mintlify.wiki/Seyamalam/bun-scikit/api/neighbors/kneighbors-regressor.md): K-Nearest Neighbors regressor for regression tasks - [ColumnTransformer](https://mintlify.wiki/Seyamalam/bun-scikit/api/pipeline/column-transformer.md): Apply different transformers to different columns - [FeatureUnion](https://mintlify.wiki/Seyamalam/bun-scikit/api/pipeline/feature-union.md): Concatenate results of multiple transformer objects - [Pipeline](https://mintlify.wiki/Seyamalam/bun-scikit/api/pipeline/pipeline.md): Chain transformers and estimators into a single workflow - [LabelEncoder](https://mintlify.wiki/Seyamalam/bun-scikit/api/preprocessing/label-encoder.md): Encode target labels with values between 0 and n_classes-1 - [MinMaxScaler](https://mintlify.wiki/Seyamalam/bun-scikit/api/preprocessing/minmax-scaler.md): Scale features to a given range, typically [0, 1] - [Normalizer](https://mintlify.wiki/Seyamalam/bun-scikit/api/preprocessing/normalizer.md): Normalize samples individually to unit norm - [OneHotEncoder](https://mintlify.wiki/Seyamalam/bun-scikit/api/preprocessing/one-hot-encoder.md): Encode categorical features as a one-hot numeric array - [PolynomialFeatures](https://mintlify.wiki/Seyamalam/bun-scikit/api/preprocessing/polynomial-features.md): Generate polynomial and interaction features - [RobustScaler](https://mintlify.wiki/Seyamalam/bun-scikit/api/preprocessing/robust-scaler.md): Scale features using statistics that are robust to outliers - [SimpleImputer](https://mintlify.wiki/Seyamalam/bun-scikit/api/preprocessing/simple-imputer.md): Imputation transformer for completing missing values - [StandardScaler](https://mintlify.wiki/Seyamalam/bun-scikit/api/preprocessing/standard-scaler.md): Standardize features by removing the mean and scaling to unit variance - [LinearSVC](https://mintlify.wiki/Seyamalam/bun-scikit/api/svm/linear-svc.md): Linear Support Vector Classification - [OneClassSVM](https://mintlify.wiki/Seyamalam/bun-scikit/api/svm/one-class-svm.md): Unsupervised outlier detection using Support Vector Machines - [SVC](https://mintlify.wiki/Seyamalam/bun-scikit/api/svm/svc.md): Support Vector Classification - [SVR](https://mintlify.wiki/Seyamalam/bun-scikit/api/svm/svr.md): Support Vector Regression - [AdaBoost](https://mintlify.wiki/Seyamalam/bun-scikit/api/tree-ensemble/adaboost.md): AdaBoostClassifier and AdaBoostRegressor API reference - [Decision Tree](https://mintlify.wiki/Seyamalam/bun-scikit/api/tree-ensemble/decision-tree.md): DecisionTreeClassifier and DecisionTreeRegressor API reference - [Gradient Boosting](https://mintlify.wiki/Seyamalam/bun-scikit/api/tree-ensemble/gradient-boosting.md): GradientBoostingClassifier and GradientBoostingRegressor API reference - [Isolation Forest](https://mintlify.wiki/Seyamalam/bun-scikit/api/tree-ensemble/isolation-forest.md): IsolationForest API reference for anomaly detection - [Random Forest](https://mintlify.wiki/Seyamalam/bun-scikit/api/tree-ensemble/random-forest.md): RandomForestClassifier and RandomForestRegressor API reference - [Model Evaluation](https://mintlify.wiki/Seyamalam/bun-scikit/concepts/model-evaluation.md): Learn how to evaluate and measure the performance of your machine learning models - [Model Training](https://mintlify.wiki/Seyamalam/bun-scikit/concepts/model-training.md): Understand the fit/predict pattern and how to train machine learning models in bun-scikit - [Building ML Pipelines](https://mintlify.wiki/Seyamalam/bun-scikit/concepts/pipelines.md): Learn how to create end-to-end machine learning workflows with bun-scikit pipelines - [Data Preprocessing & Scaling](https://mintlify.wiki/Seyamalam/bun-scikit/concepts/preprocessing.md): Learn how to prepare and transform your data using bun-scikit's preprocessing tools - [Clustering Algorithms](https://mintlify.wiki/Seyamalam/bun-scikit/guides/clustering.md): Unsupervised learning with K-Means, DBSCAN, and hierarchical clustering - [Dimensionality Reduction](https://mintlify.wiki/Seyamalam/bun-scikit/guides/dimensionality-reduction.md): PCA, SVD, t-SNE, and manifold learning techniques in bun-scikit - [Linear Models](https://mintlify.wiki/Seyamalam/bun-scikit/guides/linear-models.md): Complete guide to linear and logistic regression in bun-scikit - [Model Selection & Validation](https://mintlify.wiki/Seyamalam/bun-scikit/guides/model-selection.md): Cross-validation, hyperparameter tuning, and model evaluation in bun-scikit - [Tree-Based Models & Ensembles](https://mintlify.wiki/Seyamalam/bun-scikit/guides/tree-ensembles.md): Decision trees, random forests, and gradient boosting in bun-scikit - [Native Zig Acceleration](https://mintlify.wiki/Seyamalam/bun-scikit/guides/zig-acceleration.md): Enable high-performance native kernels for 10-100x speedup in bun-scikit - [Installation](https://mintlify.wiki/Seyamalam/bun-scikit/installation.md): Install bun-scikit and configure native Zig acceleration - [Introduction](https://mintlify.wiki/Seyamalam/bun-scikit/introduction.md): Scikit-learn-inspired machine learning for Bun + TypeScript, with native Zig acceleration - [Performance Benchmarks](https://mintlify.wiki/Seyamalam/bun-scikit/performance/benchmarks.md): Comprehensive benchmarks comparing bun-scikit with Python's scikit-learn - [Native Runtime](https://mintlify.wiki/Seyamalam/bun-scikit/performance/native-runtime.md): Understanding bun-scikit's native Zig acceleration and prebuilt binaries - [Performance Optimization Tips](https://mintlify.wiki/Seyamalam/bun-scikit/performance/optimization-tips.md): Best practices for maximizing bun-scikit performance in your applications - [Quickstart](https://mintlify.wiki/Seyamalam/bun-scikit/quickstart.md): Train your first machine learning model with bun-scikit in under 5 minutes - [Changelog](https://mintlify.wiki/Seyamalam/bun-scikit/resources/changelog.md): Release history and notable changes in bun-scikit - [Contributing](https://mintlify.wiki/Seyamalam/bun-scikit/resources/contributing.md): Guide for contributing to bun-scikit development - [Scikit-learn Parity Matrix](https://mintlify.wiki/Seyamalam/bun-scikit/resources/sklearn-parity.md): Track bun-scikit API coverage compared to scikit-learn - [Support](https://mintlify.wiki/Seyamalam/bun-scikit/resources/support.md): Get help and connect with the bun-scikit community