A comprehensive machine learning library providing supervised and unsupervised learning algorithms with consistent APIs and extensive tools for data preprocessing, model evaluation, and deployment.
87
Pending
Does it follow best practices?
Impact
87%
0.98xAverage score across 10 eval scenarios
Pending
The risk profile of this skill
Incremental and online learning via partial_fit and warm_start-enabled estimators
Partial_fit estimator
100%
100%
Classes registration
100%
100%
Streaming updates
100%
100%
Warm-start resume
100%
100%
Predict & score
100%
75%
Supervised learning algorithms (linear models, SVMs, trees, ensembles, neighbors, naive Bayes, Gaussian processes)
Linear estimator
100%
100%
Ensemble estimator
100%
100%
Probability outputs
100%
100%
Accuracy scoring
100%
100%
Model workflow
100%
100%
Unified estimator API across fit/predict/transform methods
Predictor fit/predict
100%
50%
Transformer fit/transform
100%
48%
Combined reuse
100%
75%
Fit precondition
66%
46%
Argument consistency
100%
100%
Advanced unsupervised anomaly detection and covariance methods (IsolationForest, OneClassSVM, LOF, MinCovDet, biclustering)
Global detector
100%
100%
Local outliers
100%
50%
Robust covariance
100%
100%
Biclustering
100%
100%
Sklearn-first flow
100%
100%
Feature selection utilities (filters, model-based selectors, RFE, mutual information)
Mutual info selector
33%
100%
Selector support usage
20%
66%
Model-based selection
32%
0%
Fraction thresholding
50%
100%
Recursive elimination
100%
100%
Data preprocessing and feature engineering transformers (scaling, encoding, imputation, polynomial features, kernel approximation, feature extraction)
Numeric imputer
100%
100%
Numeric scaling
100%
100%
Polynomial terms
0%
100%
Categorical imputer
100%
100%
One-hot encoding
100%
100%
Column composition
0%
100%
Multiclass and multioutput strategies (OvR/OvO, error-correcting codes, classifier/regressor chains)
OvR trainer
40%
20%
Prob thresholds
100%
53%
Chain reducer
100%
72%
Pairwise votes
100%
100%
Deterministic fit
100%
100%
Unsupervised clustering and dimensionality reduction (k-means/DBSCAN/mixtures, PCA/ICA/NMF, manifold learning)
Scaling + PCA
100%
100%
Mixture selection
100%
100%
Soft predictions
100%
100%
Manifold embedding
50%
60%
Validation errors
100%
100%
Model selection and evaluation (cross-validation splitters, Grid/Randomized/SuccessiveHalving search, metrics and learning curves)
Splitter usage
100%
100%
CV scoring
100%
100%
Search setup
100%
100%
Learning curve
100%
100%
Result bundle
100%
100%
Probabilistic modeling with Gaussian processes, mixture-based density estimation, and probability calibration
GP regressor
100%
100%
Smoothness control
100%
100%
Mixture density
100%
100%
Calibrated classifier
100%
100%
Probability outputs
100%
100%
Table of Contents