Scenario 1

Probabilistic modeling with Gaussian processes, mixture-based density estimation, and probability calibration

Scenario 2

Unsupervised clustering and dimensionality reduction (k-means/DBSCAN/mixtures, PCA/ICA/NMF, manifold learning)

Scenario 3

Feature selection utilities (filters, model-based selectors, RFE, mutual information)

Scenario 4

Supervised learning algorithms (linear models, SVMs, trees, ensembles, neighbors, naive Bayes, Gaussian processes)

Scenario 5

Advanced unsupervised anomaly detection and covariance methods (IsolationForest, OneClassSVM, LOF, MinCovDet, biclustering)

Scenario 6

Incremental and online learning via partial_fit and warm_start-enabled estimators

Scenario 7

Model selection and evaluation (cross-validation splitters, Grid/Randomized/SuccessiveHalving search, metrics and learning curves)

Scenario 8

Data preprocessing and feature engineering transformers (scaling, encoding, imputation, polynomial features, kernel approximation, feature extraction)

Scenario 9

Unified estimator API across fit/predict/transform methods

Scenario 10

Multiclass and multioutput strategies (OvR/OvO, error-correcting codes, classifier/regressor chains)