Implementation:Online ml River Datasets Higgs

Knowledge Sources	Online_ml_River
Domains	Online_Learning, Datasets, Binary_Classification, Physics
Last Updated	2026-02-08 16:00 GMT

Overview

Concrete dataset for binary classification provided by the River library.

Description

The Higgs dataset contains data produced using Monte Carlo simulations for particle physics research. The first 21 features (columns 2-22) are kinematic properties measured by the particle detectors in the accelerator. The last seven features are functions of the first 21 features; these are high-level features derived by physicists to help discriminate between the two classes.

This dataset contains 11,000,000 samples with 28 features for binary classification tasks.

Usage

This dataset is useful for:

• Large-scale binary classification problems
• Evaluating streaming algorithms on high-volume data
• Physics-based machine learning applications
• Benchmarking classifier performance on real scientific data

Code Reference

Source Location

• Repository: Online_ml_River
• File: river/datasets/higgs.py

Signature

class Higgs(base.RemoteDataset):
    def __init__(self):
        super().__init__(
            n_samples=11_000_000,
            n_features=28,
            task=base.BINARY_CLF,
            url="https://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz",
            size=2_816_407_858,
            unpack=False,
        )

    def _iter(self):
        features = [
            "lepton pT", "lepton eta", "lepton phi",
            "missing energy magnitude", "missing energy phi",
            "jet 1 pt", "jet 1 eta", "jet 1 phi", "jet 1 b-tag",
            "jet 2 pt", "jet 2 eta", "jet 2 phi", "jet 2 b-tag",
            "jet 3 pt", "jet 3 eta", "jet 3 phi", "jet 3 b-tag",
            "jet 4 pt", "jet 4 eta", "jet 4 phi", "jet 4 b-tag",
            "m_jj", "m_jjj", "m_lv", "m_jlv", "m_bb", "m_wbb", "m_wwbb",
        ]
        return stream.iter_csv(
            self.path,
            fieldnames=["is_signal", *features],
            target="is_signal",
            converters={
                "is_signal": lambda x: x.startswith("1"),
                **{f: float for f in features},
            },
        )

Import

from river import datasets
dataset = datasets.Higgs()

I/O Contract

Inputs

Name	Type	Required	Description
(none)	—	—	No parameters needed

Outputs

Name	Type	Description
iter()	tuple(dict, bool)	Yields (features_dict, target) pairs where target indicates signal vs background

Dataset Properties

Property	Value
Number of samples	11,000,000
Number of features	28
Task	Binary classification
Format	CSV (compressed)
Size	2,816,407,858 bytes

Features

The dataset includes 28 features:

• Low-level features (21): Kinematic properties measured by particle detectors (lepton properties, missing energy, jet properties)
• High-level features (7): Derived features computed by physicists (m_jj, m_jjj, m_lv, m_jlv, m_bb, m_wbb, m_wwbb)

Usage Examples

from river import datasets

dataset = datasets.Higgs()
for x, y in dataset:
    print(x, y)
    break

References

• UCI page

Related Pages

• Environment:Online_ml_River_Python_Runtime_Environment