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* ID :  [https://www.wikidata.org/wiki/Q1026367 Q1026367]

2020년 12월 26일 (토) 04:48 판

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  • In this tutorial, we'll discuss the details of generating different synthetic datasets using Numpy and Scikit-learn libraries.[1]
  • Before we show you how scikit-learn works, it’s work discussing which ML framework to use.[2]
  • scikit-learn is designed to run on one server.[2]
  • The chart is not really comprehensive, as I focused on scikit-learn.[3]
  • The chart above includes the intersection of all algorithms that are in scikit-learn and the ones that I find most useful in practice.[3]
  • Here we give a quick introduction to scikit-learn as well as to machine-learning basics.[4]
  • Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning.[5]
  • Instead of the traditional deployment route, you can also use the no-code deployment feature (preview) for scikit-learn.[6]
  • sklearn contains simple and efficient tools for data mining and data analysis.[7]
  • Scikit-learn has a number of functions to perform feature selection.[8]
  • Scikit-learn provides straightforward APIs for common ensembling approaches so data scientists can easily get up and running.[9]
  • Though it delivers better results, the boosted scikit-learn SVR is much slower to train and use.[9]
  • Check out cuML and scikit-learn on Github and file a feature request or contribute a pull request.[9]
  • To implement linear classification, we will use the SGDClassifier from scikit-learn.[10]
  • The fit function is probably the most important one in scikit-learn.[10]
  • RUNTIME_VERSION - You must specify a AI Platform Training runtime version that supports scikit-learn.[11]
  • - You must specify a AI Platform Training runtime version that supports scikit-learn.[11]
  • Scikit-learn is a library in Python that provides many unsupervised and supervised learning algorithms.[12]
  • Scikits are Python-based scientific toolboxes built around SciPy, the Python library for scientific computing.[13]
  • Among the areas Scikit-learn does not cover are deep learning, reinforcement learning, graphical models, and sequence prediction.[13]
  • Here again, Scikit-learn serves up all of the tasty classic dishes you would expect at this smorgasbord.[13]
  • My installation of Scikit-learn may well have been my easiest machine learning framework installation ever.[13]
  • This scikit contains modules specifically for machine learning and data mining, which explains the second component of the library name.[14]
  • To load in the data, you import the module datasets from sklearn .[14]
  • Scikit-learn is probably the most useful library for machine learning in Python.[15]
  • Please note that sklearn is used to build machine learning models.[15]
  • Scikit-learn comes loaded with a lot of features.[15]
  • and there is a very high chance that it is part of scikit-learn.[15]
  • The first step, with Scikit-learn, is to call the logistic regression estimator and save it as an object.[16]
  • Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python.[17]
  • It was originally called scikits.learn and was initially developed by David Cournapeau as a Google summer of code project in 2007.[17]
  • Scikit-learn is a community effort and anyone can contribute to it.[17]
  • Scikit-learn is largely written in Python, and uses numpy extensively for high-performance linear algebra and array operations.[18]
  • Scikit-learn was initially developed by David Cournapeau as a Google summer of code project in 2007.[18]
  • The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn.[19]
  • Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.[20]
  • Scikit-learn plotting capabilities (i.e., functions start with plot_ and classes end with "Display") require Matplotlib (>= 2.1.1).[20]
  • One of the best known is Scikit-Learn, a package that provides efficient versions of a large number of common algorithms.[21]
  • Scikit-Learn is characterized by a clean, uniform, and streamlined API, as well as by very useful and complete online documentation.[21]

소스

  1. ↑ scikit-learn
  2. ↑ 2.0 2.1 scikit-learn Classification Tutorial
  3. ↑ 3.0 3.1 Machine Learning Cheat Sheet (for scikit-learn)
  4. ↑ Scikit-learn: Machine Learning Without Learning the Machinery: GetMobile: Mobile Computing and Communications: Vol 19, No 1
  5. ↑ Hands-on Scikit-Learn for Machine Learning Applications - Data Science Fundamentals with Python
  6. ↑ Train scikit-learn machine learning models - Azure Machine Learning
  7. ↑ scikit-learn (sklearn)
  8. ↑ 10 Things You Didn’t Know About Scikit-Learn
  9. ↑ 9.0 9.1 9.2 From Hours to Seconds: 100x Faster Boosting, Bagging, and Stacking with RAPIDS cuML and Scikit-learn Machine Learning Model Ensembling
  10. ↑ 10.0 10.1 Learning scikit-learn: Machine Learning in Python
  11. ↑ 11.0 11.1 Training with scikit-learn on AI Platform Training
  12. ↑ What is Scikit-Learn?
  13. ↑ 13.0 13.1 13.2 13.3 Review: Scikit-learn shines for simpler machine learning
  14. ↑ 14.0 14.1 Python Machine Learning: Scikit-Learn Tutorial
  15. ↑ 15.0 15.1 15.2 15.3 Scikit-Learn In Python
  16. ↑ A Beginners Guide to Scikit-Learn
  17. ↑ 17.0 17.1 17.2 Tutorialspoint
  18. ↑ 18.0 18.1 scikit-learn
  19. ↑ A Gentle Introduction to Scikit-Learn
  20. ↑ 20.0 20.1 scikit-learn/scikit-learn: scikit-learn: machine learning in Python
  21. ↑ 21.0 21.1 Python Data Science Handbook

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