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  1. Welcome to the UC Irvine Machine Learning Repository. We currently maintain 670 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!

    • Datasets

      A small classic dataset from Fisher, 1936. One of the...

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      Thank you for considering donating a dataset to the UCI...

    • About Us

      The UCI Machine Learning Repository is a collection of...

    • Link External

      The dataset must be widely known and high quality for it to...

  2. Discover datasets around the world! Datasets; Contribute Dataset. Donate New; Link External; About Us. Who We Are; Citation Metadata; Contact Information; Login. Filters. CLEAR FILTERS ... By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

  3. This data set consists of three types of entities: (a) the specification of an auto in terms of various characteristics, (b) its assigned insurance risk rating, (c) its normalized losses in use as compared to other cars. The second rating corresponds to the degree to which the auto is more risky than its price indicates.

  4. Repository for Analysis of data hosted on UCI Machine Learning Archives - rupakc/UCI-Data-Analysis

  5. 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.

  6. **UCI Machine Learning Repository** is a collection of over 550 datasets. Some tasks are inferred based on the benchmarks list. The benchmarks section lists all benchmarks using a given dataset or any of its variants. We use variants to distinguish between results evaluated on slightly different versions of the same dataset.

  7. 4 wrz 2024 · The UCI Machine Learning Repository hosts a wide variety of datasets, each suited for different types of machine learning tasks: Classification : Datasets where the goal is to predict a categorical label (e.g., Iris dataset, Breast Cancer Wisconsin dataset).

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