Name: tessl/pypi-scikit-learn
Rating: 0.87 (1 reviews)
Author: tessl

tessl/pypi-scikit-learn

A comprehensive machine learning library providing supervised and unsupervised learning algorithms with consistent APIs and extensive tools for data preprocessing, model evaluation, and deployment.

0.98x

Overview

Eval results

Files

Evaluation results

95%

-5%

Streaming Classifier

Incremental and online learning via partial_fit and warm_start-enabled estimators

Criteria

Without context

With context

Partial_fit estimator

100%

Classes registration

100%

Streaming updates

100%

Warm-start resume

100%

Predict & score

100%

75%

100%

Binary Training Utility

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

Criteria

Without context

With context

Linear estimator

100%

Ensemble estimator

100%

Probability outputs

100%

Accuracy scoring

100%

Model workflow

100%

59%

-36%

Unified Estimator Workflow

Unified estimator API across fit/predict/transform methods

Criteria

Without context

With context

Predictor fit/predict

100%

50%

Transformer fit/transform

100%

48%

Combined reuse

100%

75%

Fit precondition

66%

46%

Argument consistency

100%

90%

-10%

Anomaly Analysis Toolkit

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

Criteria

Without context

With context

Global detector

100%

Local outliers

100%

50%

Robust covariance

100%

Biclustering

100%

Sklearn-first flow

100%

70%

24%

Feature Selection Utility

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

Criteria

Without context

With context

Mutual info selector

33%

100%

Selector support usage

20%

66%

Model-based selection

32%

Fraction thresholding

50%

100%

Recursive elimination

100%

40%

Tabular Preprocessing Helper

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

Criteria

Without context

With context

Numeric imputer

100%

Numeric scaling

100%

Polynomial terms

100%

Categorical imputer

100%

One-hot encoding

100%

Column composition

100%

66%

-19%

Multilabel Strategy Playground

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

Criteria

Without context

With context

OvR trainer

40%

20%

Prob thresholds

100%

53%

Chain reducer

100%

72%

Pairwise votes

100%

Deterministic fit

100%

92%

Probabilistic Dimensionality Reduction and Clustering

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

Criteria

Without context

With context

Scaling + PCA

100%

Mixture selection

100%

Soft predictions

100%

Manifold embedding

50%

60%

Validation errors

100%

Cross-Validated Model Selection Workflow

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

Criteria

Without context

With context

Splitter usage

100%

CV scoring

100%

Search setup

100%

Learning curve

100%

Result bundle

100%

Probabilistic Reliability Suite

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

Criteria

Without context

With context

GP regressor

100%

Smoothness control

100%

Mixture density

100%

Calibrated classifier

100%

Probability outputs

100%

Install with Tessl CLI

npx tessl i tessl/pypi-scikit-learn

Evaluated: about 2 months ago
Agent: Claude Code

Table of Contents

Streaming Classifier Binary Training Utility Unified Estimator Workflow Anomaly Analysis Toolkit Feature Selection Utility Tabular Preprocessing Helper Multilabel Strategy Playground Probabilistic Dimensionality Reduction and Clustering Cross-Validated Model Selection Workflow Probabilistic Reliability Suite