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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 ...
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.
AIDA has been described in detail in the medical / scientific / computing / diabetes literature. [9] It incorporates a compartmental model that describes glucose-insulin interaction in people completely lacking endogenous insulin secretion — i.e. insulin-dependent patients with type 1 diabetes mellitus.
Movie Dataset Data for 10,000 movies. Several features for each movie are given. 10,000 Text Clustering, classification 1999 [489] G. Wiederhold Open University Learning Analytics Dataset Information about students and their interactions with a virtual learning environment. None. ~ 30,000 Text Classification, clustering, regression 2015 [490] [491]
It is free software released under the three-clause BSD license. [2] The name is derived from the term " pan el da ta ", an econometrics term for data sets that include observations over multiple time periods for the same individuals, [ 3 ] as well as a play on the phrase "Python data analysis".
Obesity dipped slightly in U.S. adults last year for the first time in more than a decade, a study found. The researchers suggested that might be due, in part, to the rise of weight loss drugs ...
Taylor says students from all different backgrounds are finding a place to learn at Taylor'd Cosmetology. "We have 60% Black, about 30% Latino and about 10% White," Taylor said.
Training process of SOM on a two-dimensional data set. This process is repeated for each input vector for a (usually large) number of cycles λ. The network winds up associating output nodes with groups or patterns in the input data set. If these patterns can be named, the names can be attached to the associated nodes in the trained net.