scikit-learn

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Artificial Intelligence and Machine Learning Bias has Dangerous Implications

Algorithms are everywhere in our world, and so is bias. From social media news feeds to streaming service recommendations to online shopping, computer algorithms—specifically, machine learning algorithms—have permeated our day-to-day world. As for bias, we need only examine the 2016 American election to understand how deeply—both implicitly and explicitly—it permeates our society as well. What’s often overlooked, however, is the intersection between these two: bias in computer algorithms themselves. Contrary to what many of us might think, technology is not objective...

Continuum Analytics Teams Up with Intel for Python Distribution Powered by Anaconda

Press Release | Continuum Analytics | September 8, 2016

Continuum Analytics, the creator and driving force behind Anaconda, the leading Open Data Science platform powered by Python, is pleased to announce a technical collaboration with Intel resulting in the Intel® Distribution for Python powered by Anaconda. Intel Distribution for Python powered by Anaconda was recently announced by Intel and will be delivered as part of Intel® Parallel Studio XE 2017 software development suite. With a common distribution for the Open Data Science community that increases Python and R performance up to 100X, Intel has empowered enterprises to build a new generation of intelligent applications that drive immediate business value...

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Python Versus R for Machine Learning and Data Analysis

Machine learning and data analysis are two areas where open source has become almost the de facto license for innovative new tools. Both the Python and R languages have developed robust ecosystems of open source tools and libraries that help data scientists of any skill level more easily perform analytical work. The distinction between machine learning and data analysis is a bit fluid, but the main idea is that machine learning prioritizes predictive accuracy over model interpretability, while data analysis emphasizes interpretability and statistical inference. Python, being more concerned with predictive accuracy, has developed a positive reputation in machine learning. R, as a language for statistical inference, has made its name in data analysis...