Feature Comparison: datarust vs sklearn

A side-by-side view of what’s implemented. ✓ = supported, — = no equivalent.

Preprocessing

Encoders

Imputation & Selection

Models & Decomposition

Model Selection & Metrics

Composition & Infrastructure

Where datarust goes beyond sklearn

• FrequencyEncoder — count/proportion encoding not in sklearn.
• Pipeline runtime editing — get_step, set_step, insert_step, remove_step for live pipeline surgery.
• JSON serialization — human-readable, language-agnostic fitted-model persistence (vs sklearn’s binary joblib/pickle).
• Type-safe categorical/numeric separation — the compiler prevents mixing StrMatrix and Matrix.

Rust ecosystem comparison

datarust is a preprocessing-first classical ML library. Within the Rust ecosystem its direct peers are smartcore and linfa. Deep-learning frameworks (candle, burn, tch-rs) solve a different problem and are out of scope here; rusty-machine has been archived and is omitted.

Design philosophies

The three libraries were built around different priorities, which shows up in every design decision:

• datarust optimizes for preprocessing depth and zero-dependency deployability. All linear algebra is pure Rust (Jacobi eigensolver, Cholesky, coordinate descent), so the default build has no external C libraries — it links cleanly into WASM, embedded targets, and CLI tools. The trade-off is algorithm breadth: only the four linear models (Linear / Ridge / Lasso / Logistic regression) and KMeans clustering are implemented so far.
• smartcore optimizes for single-crate algorithm breadth. One dependency gives you SVM, RandomForest, DecisionTree, KMeans, DBSCAN, KNN, NaiveBayes, and more, plus model selection and metrics. The trade-off is preprocessing: only StandardScaler and OneHotEncoder are provided, and the crate depends on ndarray plus a BLAS backend.
• linfa optimizes for modularity. Each algorithm family lives in its own crate (linfa-svm, linfa-trees, linfa-clustering, linfa-ensemble, …) behind a shared linfa-core trait surface, so you only compile what you use. linfa-preprocessing additionally offers scalers and text vectorizers (Count / TF-IDF). The trade-off: categorical encoders, imputers, and feature selectors are sparse or undocumented, and Ridge/Lasso exist only through linfa-elasticnet’s l1_ratio parameter.

Feature matrix

Verified against the July 2026 releases: smartcore 0.5.3, linfa 0.8.1. Legend: ✓ present, ✗ confirmed absent, ? not clearly documented at the time of writing — please open an issue or PR if a cell goes stale.

Preprocessing & Encoders

Models & Decomposition

Infrastructure

When to choose which

Complementary use

These libraries are not mutually exclusive. Because all three expose plain Vec/matrix in/out interfaces, you can mix them in a single pipeline: use datarust for preprocessing (where its coverage is unique), then hand the transformed features to a smartcore or linfa estimator for an algorithm datarust doesn’t implement yet.