Implementation:Online ml River Datasets ChickWeights

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

Overview

Concrete dataset for regression provided by the River library.

Description

Chick weights along time. The stream contains 578 items and 3 features. The goal is to predict the weight of each chick along time, according to the diet the chick is on. The data is ordered by time and then by chick.

This dataset contains 578 samples with 3 features for regression tasks.

Usage

This dataset is useful for:

• Time series regression with grouped data
• Growth prediction modeling
• Studying effects of different treatments (diets) on outcomes
• Educational examples of longitudinal data analysis

Code Reference

Source Location

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

Signature

class ChickWeights(base.FileDataset):
    def __init__(self):
        super().__init__(filename="chick-weights.csv", n_samples=578, n_features=3, task=base.REG)

    def __iter__(self):
        return stream.iter_csv(
            self.path,
            target="weight",
            converters={"time": int, "weight": int, "chick": int, "diet": int},
        )

Import

from river import datasets
dataset = datasets.ChickWeights()

I/O Contract

Inputs

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

Outputs

Name	Type	Description
iter()	tuple(dict, int)	Yields (features_dict, target) pairs where target is the chick weight

Dataset Properties

Property	Value
Number of samples	578
Number of features	3
Task	Regression
Format	CSV

Features

The dataset includes the following features:

• time: Time point (integer)
• chick: Chick identifier (integer)
• diet: Diet type (integer)
• weight: Weight of the chick (target variable, integer)

Usage Examples

from river import datasets

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

References

• Chick weight dataset overview

Related Pages

• Environment:Online_ml_River_Python_Runtime_Environment