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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 ...
Connect-4 Dataset Contains all legal 8-ply positions in the game of connect-4 in which neither player has won yet, and in which the next move is not forced. None. 67,557 Text Classification 1995 [466] J. Tromp Chess (King-Rook vs. King) Dataset Endgame Database for White King and Rook against Black King. None. 28,056 Text Classification 1994 ...
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]
[4] [5] It was created by "re-mixing" the samples from NIST's original datasets. [6] The creators felt that since NIST's training dataset was taken from American Census Bureau employees, while the testing dataset was taken from American high school students, it was not well-suited for machine learning experiments. [ 7 ]
The scikit-multiflow library is implemented under the open research principles and is currently distributed under the BSD 3-clause license. scikit-multiflow is mainly written in Python, and some core elements are written in Cython for performance. scikit-multiflow integrates with other Python libraries such as Matplotlib for plotting, scikit-learn for incremental learning methods [4 ...
t-SNE has been used for visualization in a wide range of applications, including genomics, computer security research, [3] natural language processing, music analysis, [4] cancer research, [5] bioinformatics, [6] geological domain interpretation, [7] [8] [9] and biomedical signal processing.
SAT-4 Airborne Dataset Images were extracted from the National Agriculture Imagery Program (NAIP) dataset. SAT-4 has four broad land cover classes, includes barren land, trees, grassland and a class that consists of all land cover classes other than the above three. 500,000 Images Classification 2015 [172] [173] S. Basu et al. SAT-6 Airborne ...
Scatterplot of the data set. The Iris flower data set or Fisher's Iris data set is a multivariate data set used and made famous by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis. [1]