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

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

    A 3.1 TB dataset consisting of permissively licensed source code in 30 programming languages. Filtered through license detection and deduplication. 6 TB, 51.76B files (prior to deduplication); 3 TB, 5.28B files (after). 358 programming languages. Parquet Language modeling, autocompletion, program synthesis. 2022 [402] [403]

  3. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  4. Training, validation, and test data sets - Wikipedia

    en.wikipedia.org/wiki/Training,_validation,_and...

    A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]

  5. 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]

  6. Symbolic regression - Wikipedia

    en.wikipedia.org/wiki/Symbolic_regression

    Evolutionary Forest is a Genetic Programming-based automated feature construction algorithm for symbolic regression. [15] [16] uDSR is a deep learning framework for symbolic optimization tasks [17] dCGP, differentiable Cartesian Genetic Programming in python (free, open source) [18] [19]

  7. Dask (software) - Wikipedia

    en.wikipedia.org/wiki/Dask_(software)

    Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.

  8. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    The Raspberry Pi single-board computer project has adopted Python as its main user-programming language. LibreOffice includes Python and intends to replace Java with Python. Its Python Scripting Provider is a core feature [234] since Version 4.0 from 7 February 2013.

  9. Pattern recognition - Wikipedia

    en.wikipedia.org/wiki/Pattern_recognition

    [9] [10] The last two examples form the subtopic image analysis of pattern recognition that deals with digital images as input to pattern recognition systems. [11] [12] Optical character recognition is an example of the application of a pattern classifier. The method of signing one's name was captured with stylus and overlay starting in 1990.

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