The standard Python library for classical machine learning. By 2026, scikit-learn remains the default for tabular ML, baselines, and feature engineering, freque
scikit-learn is the canonical classical-ML library: clean APIs for regression, classification, clustering, and pipelines that remain the right tool for tabular problems, often beating LLM-era complexity on cost, latency, and explainability for structured data.
Pricing: Free and open-source (BSD).
No: for tabular prediction (churn, risk, demand), gradient boosting and friends routinely beat LLM approaches on accuracy-per-dollar and explainability. Use LLMs for language; sklearn-style ML for tables.
It teaches the discipline, splits, validation, metrics, pipelines, that transfers to all ML including LLM evaluation, with the gentlest learning curve in the field.