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  2. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Machine learningand data mining. These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning. Major advances in this field can result from advances in learning algorithms (such as deep learning ), computer hardware, and, less ...

  3. Iris flower data set - Wikipedia

    en.wikipedia.org/wiki/Iris_flower_data_set

    The iris data set is widely used as a beginner's dataset for machine learning purposes. The dataset is included in R base and Python in the machine learning library scikit-learn, so that users can access it without having to find a source for it. Several versions of the dataset have been published. [8]

  4. Kaggle - Wikipedia

    en.wikipedia.org/wiki/Kaggle

    Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.

  5. Anscombe's quartet - Wikipedia

    en.wikipedia.org/wiki/Anscombe's_quartet

    Anscombe's quartet comprises four datasets that have nearly identical simple descriptive statistics, yet have very different distributions and appear very different when graphed. Each dataset consists of eleven ( x , y) points. They were constructed in 1973 by the statistician Francis Anscombe to demonstrate both the importance of graphing data ...

  6. Leakage (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Leakage_(machine_learning)

    t. e. In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which would not be expected to be available at prediction time, causing the predictive scores (metrics) to overestimate the model's utility when run in a production environment. [ 1]

  7. Multiclass classification - Wikipedia

    en.wikipedia.org/wiki/Multiclass_classification

    v. t. e. In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification ). For example, deciding on whether an image is showing a banana, an orange ...

  8. Symbolic regression - Wikipedia

    en.wikipedia.org/wiki/Symbolic_regression

    Symbolic regression. Expression tree as it can be used in symbolic regression to represent a function. Symbolic regression ( SR) is a type of regression analysis that searches the space of mathematical expressions to find the model that best fits a given dataset, both in terms of accuracy and simplicity. No particular model is provided as a ...

  9. MNIST database - Wikipedia

    en.wikipedia.org/wiki/MNIST_database

    MNIST database. The MNIST database ( Modified National Institute of Standards and Technology database[ 1]) is a large database of handwritten digits that is commonly used for training various image processing systems. [ 2][ 3] The database is also widely used for training and testing in the field of machine learning. [ 4][ 5] It was created by ...