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

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

    The datasets are classified, based on the licenses, as Open data and Non-Open data. The datasets from various governmental-bodies are presented in List of open government data sites. The datasets are ported on open data portals. They are made available for searching, depositing and accessing through interfaces like Open API. The datasets are ...

  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. 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 ...

  5. Gower's distance - Wikipedia

    en.wikipedia.org/wiki/Gower's_distance

    In statistics, Gower's distance between two mixed-type objects is a similarity measure that can handle different types of data within the same dataset and is particularly useful in cluster analysis or other multivariate statistical techniques. Data can be binary, ordinal, or continuous variables.

  6. 10 Critical Steps to Writing ChatGPT Prompts for Beginners - AOL

    www.aol.com/10-critical-steps-writing-chatgpt...

    Provide a ‌[Python] script to handle missing values in my dataset using ‌[pandas]. Give me a basic example of building a ‌[logistic regression model] using ‌[scikit-learn].

  7. Oversampling and undersampling in data analysis - Wikipedia

    en.wikipedia.org/wiki/Oversampling_and_under...

    A variety of data re-sampling techniques are implemented in the imbalanced-learn package [1] compatible with the scikit-learn Python library. The re-sampling techniques are implemented in four different categories: undersampling the majority class, oversampling the minority class, combining over and under sampling, and ensembling sampling.

  8. Calinski–Harabasz index - Wikipedia

    en.wikipedia.org/wiki/Calinski–Harabasz_index

    Given a data set of n points: {x 1, ..., x n}, and the assignment of these points to k clusters: {C 1, ..., C k}, the Calinski–Harabasz (CH) Index is defined as the ratio of the between-cluster separation (BCSS) to the within-cluster dispersion (WCSS), normalized by their number of degrees of freedom:

  9. MNIST database - Wikipedia

    en.wikipedia.org/wiki/MNIST_database

    Extended MNIST (EMNIST) is a newer dataset developed and released by NIST to be the (final) successor to MNIST. [ 15 ] [ 16 ] MNIST included images only of handwritten digits. EMNIST includes all the images from NIST Special Database 19 (SD 19), which is a large database of 814,255 handwritten uppercase and lower case letters and digits.