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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 ...
CSV and PDF Natural language processing, QnA 2021 The Atticus Project: Vietnamese Image Captioning Dataset (UIT-ViIC) Vietnamese Image Captioning Dataset 19,250 captions for 3,850 images CSV and PDF Natural language processing, Computer vision 2020 [112] Lam et al. Vietnamese Names annotated with Genders (UIT-ViNames)
The following outline is provided as an overview of, and topical guide to, machine learning: . Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1]
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1]
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.
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
WASHINGTON (Reuters) -The number of Americans filing new applications for jobless benefits fell more than expected last week, reversing the prior week's jump and suggesting that a gradual labor ...
scikit-learn includes a Python implementation of DBSCAN for arbitrary Minkowski metrics, which can be accelerated using k-d trees and ball trees but which uses worst-case quadratic memory. A contribution to scikit-learn provides an implementation of the HDBSCAN* algorithm.