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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]
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.
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 ...
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 ...
Python: the KernelReg class for mixed data types in the statsmodels.nonparametric sub-package (includes other kernel density related classes), the package kernel_regression as an extension of scikit-learn (inefficient memory-wise, useful only for small datasets) R: the function npreg of the np package can perform kernel regression. [7] [8]
Product One-way Two-way MANOVA GLM Mixed model Post-hoc Latin squares; ADaMSoft: Yes Yes No No No No No Alteryx: Yes Yes Yes Yes Yes Analyse-it: Yes Yes No
scikit-learn – extends SciPy with a host of machine learning models (classification, clustering, regression, etc.) Shogun (toolbox) – open-source, large-scale machine learning toolbox that provides several SVM (Support Vector Machine) implementations (like libSVM, SVMlight) under a common framework and interfaces to Octave, MATLAB, Python, R
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: