Browse Datasets
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Iris
A small classic dataset from Fisher, 1936. One of the earliest known datasets used for evaluating classification methods.
Mushroom
From Audobon Society Field Guide; mushrooms described in terms of physical characteristics; classification: poisonous or edible
Abalone
Predict the age of abalone from physical measurements
Zoo
Artificial, 7 classes of animals
Covertype
Classification of pixels into 7 forest cover types based on attributes such as elevation, aspect, slope, hillshade, soil-type, and more.
Secondary Mushroom
Dataset of simulated mushrooms for binary classification into edible and poisonous.
Jute Pest
This dataset has 17 classes. Data are divided in three partition train, val and test. The classes are 0 : Beet Armyworm 1 : Black Hairy 2 : Cutworm 3 : Field Cricket 4 : Jute Aphid 5 : Jute Hairy 6 : Jute Red Mite 7 : Jute Semilooper 8 : Jute Stem Girdler 9 : Jute Stem Weevil 10 : Leaf Beetle 11 : Mealybug 12 : Pod Borer 13 : Scopula Emissaria 14 : Termite 15 : Termite odontotermes (Rambur) 16 : Yellow Mite
Palmer Penguins
An introductory dataset presented as an alternative to Iris and useful for teaching data exploration/visualization. Data comes from 3 penguin species in the islands of Palmer Archipelago, Antarctica.
Audiology (Standardized)
Standardized version of the original audiology database
ApisTox
ApisTox is a dataset focusing on the toxicity of pesticides to honey bees (Apis mellifera). This dataset combines and leverages data from existing sources such as ECOTOX and PPDB, providing an extensive, consistent, and curated collection that surpasses the previous datasets. ApisTox incorporates a wide array of data, including toxicity levels for chemicals, details such as time of their publication in literature, and identifiers linking them to external chemical databases. This dataset may serve as an important tool for environmental and agricultural research, but also can support the development of policies and practices aimed at minimizing harm to bee populations. Finally, ApisTox offers a unique resource for benchmarking molecular property prediction methods on agrochemical compounds, facilitating advancements in both environmental science and cheminformatics. Code used to produce the dataset is available at https://github.com/j-adamczyk/apis_tox_dataset
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