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scikit-learn - Wikipedia 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
David Cournapeau - Wikipedia The scikit-learn project started as scikits learn, a Google Summer of Code project by David Cournapeau After having worked for Silveregg, a SaaS Japanese company delivering recommendation systems for Japanese online retailers, [3] he worked for 6 years at Enthought, a scientific consulting company He joined Cogent Labs, a Japanese Deep Learning AI company, in 2017 [4] He is a Machine
Lists of open-source artificial intelligence software - Wikipedia These are lists include projects which release at least some of their software under open-source licenses and are related to artificial intelligence projects These include software libraries, frameworks, platforms, and tools used for machine learning, deep learning, natural language processing, computer vision, reinforcement learning, artificial general intelligence, and more
Mixture model - Wikipedia PyMix – Python Mixture Package, algorithms and data structures for a broad variety of mixture model based data mining applications in Python sklearn mixture – A module from the scikit-learn Python library for learning Gaussian Mixture Models (and sampling from them), previously packaged with SciPy and now packaged as a SciKit
Isolation forest - Wikipedia Python implementation with Scikit-learn The isolation forest algorithm is commonly used by data scientists through the version made available in the scikit-learn library The snippet below depicts a brief implementation of an isolation forest, with direct explanations with comments
Category:Python (programming language) scientific libraries - Wikipedia M Matplotlib Mlpy MNE-Python N NetworkX NeuroKit NumPy O OceanParcels P Pandas (software) ProbLog PsychoPy Pvlib python PyMC PyTorch PyTorch Lightning R RDKit S Sage Manifolds SageMath Scikit-image Scikit-learn Scikit-multiflow SciPy SymPy T TensorFlow Theano (software) TomoPy
Multi-label classification - Wikipedia The scikit-learn Python package implements some multi-labels algorithms and metrics The scikit-multilearn Python package specifically caters to the multi-label classification It provides multi-label implementation of several well-known techniques including SVM, kNN and many more The package is built on top of scikit-learn ecosystem
Oversampling and undersampling in data analysis - Wikipedia 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
Random projection - Wikipedia Implementations RandPro - An R package for random projection [15][16] sklearn random_projection - A module for random projection from the scikit-learn Python library Weka implementation [1]