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- Home - UCI Machine Learning Repository
Welcome to the UC Irvine Machine Learning Repository We currently maintain 678 datasets as a service to the machine learning community Here, you can donate and find datasets used by millions of people all around the world!
- UCI Machine Learning Repository
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- About - UCI Machine Learning Repository
The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms
- Iris - UCI Machine Learning Repository
This is one of the earliest datasets used in the literature on classification methods and widely used in statistics and machine learning The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant
- UCI Machine Learning Repository
This dataset comprises molecular descriptors generated using RDKit, specifically curated for the study of drug-induced autoimmunity through ensemble machine learning approaches
- Heart Disease - UCI Machine Learning Repository
Discover datasets around the world!This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them In particular, the Cleveland database is the only one that has been used by ML researchers to date The "goal" field refers to the presence of heart disease in the patient It is integer valued from 0 (no presence) to 4 Experiments with the Cleveland
- UCI Machine Learning Repository
Heterogeneity Activity Recognition The Heterogeneity Human Activity Recognition (HHAR) dataset from Smartphones and Smartwatches is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc ) in real-world contexts; specifically, the dataset is gathered with a variety of different device models and
- Breast Cancer Wisconsin (Diagnostic) - UCI Machine Learning Repository
Dataset Files Papers Citing this Dataset Sort by Year, desc Machine learning in medicine: a practical introduction By Jenni Sidey-Gibbons, Chris Sidey-Gibbons 2019 Published in BMC medical research methodology
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