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Free GeneXus: GeneXus Cross Platform (multiple) 1991 v17 Proprietary: Genshi (templating language) Edgewall Software cross-platform (Python) 2006-08-03 0.5.1 2008-07-09 Jinja (Template engine) Pocoo team cross-platform (Python) 2.1.1 BSD: Kid (templating language) Ryan Tomayko cross-platform (Python) 0.9.6 2006-12-20 Mako: Michael Bayer
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 ...
Pages in category "Free software programmed in Python" The following 200 pages are in this category, out of approximately 313 total. This list may not reflect recent changes .
Thus one is always free to choose the statistic which best discriminates between hypothesis and alternative and which minimizes losses. Permutation tests can be used for analyzing unbalanced designs [4] and for combining dependent tests on mixtures of categorical, ordinal, and metric data (Pesarin, 2001) [citation needed]. They can also be used ...
Pandas (styled as pandas) is a software library written for the Python programming language for data manipulation and analysis.In particular, it offers data structures and operations for manipulating numerical tables and time series.
Also, purity doesn't work well for imbalanced data, where even poorly performing clustering algorithms will give a high purity value. For example, if a size 1000 dataset consists of two classes, one containing 999 points and the other containing 1 point, then every possible partition will have a purity of at least 99.9%.
To convolutionally encode data, start with k memory registers, each holding one input bit.Unless otherwise specified, all memory registers start with a value of 0. The encoder has n modulo-2 adders (a modulo 2 adder can be implemented with a single Boolean XOR gate, where the logic is: 0+0 = 0, 0+1 = 1, 1+0 = 1, 1+1 = 0), and n generator polynomials — one for each adder (see figure below).