# Roadmap
The canonical, detailed roadmap lives in [`ROADMAP.md`][repo] at the repository
root. This page gives a higher-level tour of *why* each phase is ordered the way
it is, and what it unlocks for users.
[repo]: https://github.com/genc-murat/datarust/blob/main/ROADMAP.md
## The destination
datarust aims to be the **scikit-learn of Rust** for classical, CPU-bound
machine learning: the library you reach for when you need preprocessing,
trees, clustering, SVM, cross-validation, and text features — without pulling
in BLAS, a Python runtime, or a GPU stack.
The principles that shape every decision:
- **Zero dependencies by default.** Every algorithm ships as pure Rust.
- **CPU-first.** GPU and deep learning are served by [candle] and [burn].
- **scikit-learn API parity** where Rust allows it; type-safe improvements
where Rust enables them.
- **No panics** — public APIs return `Result`; `missing_docs` is enforced.
[candle]: https://crates.io/crates/candle-core
[burn]: https://crates.io/crates/burn
## Where we are (v0.6.1)
The preprocessing, linear-model, and clustering foundations are solid:
- **18 transformers/encoders/imputers/selectors** — the deepest preprocessing
coverage of any Rust ML library.
- **4 linear models** (Linear / Ridge / Lasso / Logistic regression) with
binary IRLS and multiclass softmax support.
- **KMeans clustering** (Lloyd's algorithm, k-means++ initialization) +
silhouette score.
- 15 metrics: regression (MSE, MAE, R², ...), classification (accuracy,
precision, recall, F1, ROC-AUC, PR-AUC, kappa, Matthews, ...), clustering
(silhouette).
- `Pipeline`, `ColumnTransformer`, cross-validation, `Params` trait.
- **Zero external dependencies** by default; `serde` / `rayon` /
`matrixmultiply` are opt-in.
What is *conspicuously absent*: trees and ensembles, hyperparameter search,
text features, and SVM. The roadmap below addresses each, in order of impact.
## The release track
### v0.7 — Tree-based learning
**Why this comes first.** Trees and ensembles are the single most requested
feature and the backbone of tabular ML. A shared, seedable RNG is a
prerequisite for bootstrap sampling in ensembles — the current private
`xorshift64` needs to be promoted first.
Highlights: `DecisionTree` (CART), `RandomForest`, `ExtraTrees`, a `Bagging`
meta-estimator, and `feature_importances_` output. A new `src/tree/` and
`src/ensemble/` module pair.
### v0.8 — Model selection & text
**Why this comes second.** Once the algorithm catalog is broader, the next
bottleneck is *tuning* and *NLP*. `GridSearchCV` builds directly on the
`Params` trait (`get_params`/`set_params`) shipped in v0.6. Text vectorizers
depend on sparse matrix arithmetic, which today does not exist — so this phase
widens `SparseMatrix` from read-only storage into a real linear-algebra type.
Highlights: `GridSearchCV`/`RandomizedSearchCV`, `validation_curve`/
`learning_curve`, `CountVectorizer`/`TfidfVectorizer`, `KNeighbors`, and
Naive Bayes — the last three together forming a complete document-classification
stack.
### v0.9 — Depth & breadth
**Why this is late.** These are high-value but lower-leverage than the
foundational phases. Many users will be productive with v0.7–0.8 alone.
Highlights: `GradientBoosting`/`AdaBoost`/`Voting`/`Stacking`, `LinearSVC`/`SVC`
(SMO solver, pure Rust), `DBSCAN`/`AgglomerativeClustering`, `ElasticNet`/
`SGDClassifier`, `NMF`/`FactorAnalysis`, an embedded `datasets` module, and a
`csv` feature flag for data loading.
### v1.0 — Stability
**Why v1.0 is mostly cleanup.** By this point the API surface has been
exercised across many estimators and is ready to be frozen. v1.0 removes
legacy `#[doc(hidden)]` APIs, refreshes `ARCHITECTURE.md`, runs a full public-API
audit, and publishes the SemVer stability commitment.
## What is deliberately out of scope
The roadmap is as much about what datarust will *not* become:
- **GPU compute and deep learning** — served by [candle] and [burn].
- **Distributed training** — a networking project, not a classical-ML one.
- **pickle/joblib compatibility** — technically impossible across languages.
- **SHAP/LIME** — permutation importance (planned) covers the common need.
A few items are **under consideration** for post-1.0 but not committed: `f32`
generics, manifold learning (`TSNE`), `HistGradientBoosting`, ONNX
export/import, NumPy `.npy` interop, and a minimal `MLPClassifier`. See
[`ROADMAP.md`][repo] for the full list and rationale.
## Contributing
The checkboxes in [`ROADMAP.md`][repo] double as a contribution guide. Pick an
unchecked item, open an issue to align on API shape, and land it with doc
comments and sklearn-parity tests. Progress is recorded in
[`CHANGELOG.md`](./changelog.md) under each release.