ferroml 1.0.1

Statistically rigorous AutoML in Rust with Python bindings

FerroML

High-performance ML in Rust with a scikit-learn-compatible Python API.

FerroML is a machine learning library written in Rust that provides 55+ algorithms with statistical rigor built in: confidence intervals on predictions, hypothesis testing for model comparison, and assumption checks on every model. It's 2-40x faster than scikit-learn on predict and up to 9x faster on fit for tree/ensemble models.

Installation

pip install ferroml

Requires Python 3.10+. Pre-built wheels available for Linux (x86_64, aarch64), macOS (x86_64, arm64), and Windows (x86_64).

Quick Start

from ferroml.linear import LinearRegression
import numpy as np

# Linear regression with full statistical diagnostics
X = np.random.randn(100, 5)
y = X @ np.array([1, 2, 3, 4, 5]) + np.random.randn(100) * 0.1

model = LinearRegression()
model.fit(X, y)
print(model.summary())  # R-style output: coefficients, std errors, p-values, R²

from ferroml.trees import RandomForestClassifier
from ferroml.preprocessing import StandardScaler
from ferroml.pipeline import Pipeline

# scikit-learn-compatible pipeline
pipe = Pipeline([
    ("scaler", StandardScaler()),
    ("clf", RandomForestClassifier(n_estimators=100)),
])
pipe.fit(X_train, y_train)
score = pipe.score(X_test, y_test)

from ferroml.preprocessing import CountVectorizer, TfidfTransformer
from ferroml.naive_bayes import MultinomialNB

# Text classification
cv = CountVectorizer()
X_counts = cv.fit_transform(documents)
tfidf = TfidfTransformer()
X_tfidf = tfidf.fit_transform(X_counts)
clf = MultinomialNB()
clf.fit(X_tfidf, y)

Performance vs scikit-learn

All benchmarks produce matching predictions. Speedup >1x = FerroML is faster.

Model	N	Fit	Predict
RandomForest	1K	9.2x	7.8x
Ridge	1K	5.3x	19.3x
DecisionTree	5K	1.4x	16.4x
GradientBoosting	1K	1.5x	1.2x
LogisticRegression	10K	1.5x	13.7x

FerroML is faster on predict universally (zero Python overhead) and on fit for tree/ensemble models (Rayon parallel construction). scikit-learn wins on fit for LAPACK/MKL-backed linear algebra.

Available Models

Module	Models
linear	LinearRegression, LogisticRegression, Ridge, Lasso, ElasticNet, RidgeCV, LassoCV, ElasticNetCV, RidgeClassifier, QuantileRegression, RobustRegression, Perceptron, IsotonicRegression
trees	DecisionTreeClassifier, DecisionTreeRegressor, GradientBoostingClassifier/Regressor, HistGradientBoostingClassifier/Regressor
ensemble	RandomForest, ExtraTrees, AdaBoost, Bagging, Stacking, Voting (classifiers + regressors), SGD, PassiveAggressive
naive_bayes	GaussianNB, MultinomialNB, BernoulliNB, CategoricalNB
svm	SVC, SVR, LinearSVC, LinearSVR
neighbors	KNeighborsClassifier/Regressor, NearestCentroid
neural	MLPClassifier, MLPRegressor
gaussian_process	GaussianProcessClassifier, GaussianProcessRegressor
clustering	KMeans, DBSCAN, HDBSCAN, AgglomerativeClustering, GaussianMixture
anomaly	IsolationForest, LocalOutlierFactor
decomposition	PCA, IncrementalPCA, TruncatedSVD, LDA, QDA, FactorAnalysis, TSNE
preprocessing	22+ transformers: scalers, encoders, imputers, SMOTE/ADASYN, CountVectorizer, TfidfTransformer
explainability	TreeSHAP, KernelSHAP, permutation importance, PDP, ICE, H-statistic
multioutput	MultiOutputClassifier, MultiOutputRegressor
calibration	TemperatureScaling, Sigmoid (Platt), Isotonic
pipeline	Pipeline, ColumnTransformer, FeatureUnion
model_selection	train_test_split, cross_validate, KFold, StratifiedKFold, GroupKFold, TimeSeriesSplit
metrics	ROC-AUC, F1, MCC, R², RMSE, MAE, roc_curve, precision_recall_curve
automl	AutoML with statistical model comparison
datasets	Iris, Diabetes, Wine, California Housing, synthetic generators

sklearn API Compatibility

FerroML supports the scikit-learn API conventions:

• fit() / predict() / transform() on all models
• score() on 56 models (R² for regressors, accuracy for classifiers)
• partial_fit() on 10 models for incremental learning
• decision_function() on 13 classifiers
• predict_proba() on probabilistic classifiers
• Pipeline and ColumnTransformer composition
• NumPy array input/output

Testing

5,650+ tests passing (3,550+ Rust + 2,100+ Python), validated against scikit-learn, scipy, xgboost, lightgbm, and statsmodels with 200+ cross-library correctness tests.

License

MIT OR Apache-2.0