Subject
13 entries
Scikit Learn
Bookmarks
Scikit-Learn Cheat Sheet (2021)
A cheat sheet for scikit-learn's main API patterns — estimator interface, preprocessing, model selection, and pipelines. Useful for quickly recalling the consistent fit/predict/transform pattern across all sklearn objects.
LazyPredict: Fit All scikit-learn Models in One Line
LazyPredict fits and evaluates all scikit-learn classifiers or regressors on a dataset with a single call, returning a sorted comparison table. A fast baseline scanner for figuring out which model family is worth investing in before tuning.
skift: scikit-learn Wrappers for fastText
skift wraps Facebook's fastText text classifiers in scikit-learn's estimator API, making fastText accessible as a drop-in component in scikit-learn pipelines and GridSearchCV. Useful for fast, production-grade text classification without leaving the sklearn ecosystem.
ELI5 — sklearn Explainability Module
ELI5's sklearn module provides model explanation tools for scikit-learn estimators — feature importance, prediction decomposition, and permutation-based importance across linear models, tree ensembles, and SVMs. The explainability companion for sklearn workflows.
sktime — Unified Machine Learning with Time Series
sktime is a Python library providing a unified scikit-learn-compatible interface for time series machine learning — forecasting, classification, regression, clustering, and anomaly detection. Solves the ecosystem fragmentation problem for temporal data.
Speeding up scikit-learn Workflow Using a High-Performance Go Proxy
A 2016 blog post on using HoverPy — a Go-based HTTP recording/replay proxy — to speed up scikit-learn workflows that make external HTTP calls. An early example of applying API mocking techniques to ML pipelines for faster, deterministic testing.
Scikit-learn Pipelines and FeatureUnions
Zac Stewart's deep dive into composing scikit-learn Pipelines and FeatureUnions — showing how to chain preprocessing steps, branch feature transformations, and combine them back together while preventing data leakage. The definitive 2014 guide to production-ready sklearn code.
Converting Categorical Data into Numbers with Pandas and Scikit-Learn
FastML tutorial on converting categorical variables to numeric form using Pandas and scikit-learn's LabelEncoder and OneHotEncoder. A foundational data preprocessing step that trips up many beginners.
How to Get Started with Machine Learning in Python
Prismatic story aggregating a 'How to Get Started with Machine Learning in Python' tutorial — a 2014 entry point to scikit-learn, NumPy, and Pandas for ML practitioners. Reflects the era's onboarding gap before dedicated ML courses existed.
Scikit-Learn: Model Validation and Testing (PyCon 2013 Notebook)
Jake VanderPlas's PyCon 2013 notebook on model validation and testing in scikit-learn — covers train/test splits, cross-validation, and model selection in executable notebook form. A practical tutorial that shaped how Python practitioners learned to evaluate models.
Troll Detection with Scikit-Learn
Impermium's Kaggle blog post on building a troll detection classifier with scikit-learn — text features, gradient boosting, and the practical challenges of training on imbalanced toxic comment data.
Data Science in Python — Yhat Tutorial
Yhat's end-to-end data science tutorial in Python using pandas for data manipulation and scikit-learn for modeling. One of the cleaner introductory pipelines from 2014, before this kind of content became ubiquitous.
sklearn-pandas: Bridge Between pandas and scikit-learn
sklearn-pandas is a library bridging pandas DataFrames and scikit-learn's pipeline API — enabling column-level transformations with named features rather than anonymous numpy arrays. Fills a friction point that frustrated every data scientist using both libraries together.