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Techniques for Collecting, Prepping, and Plotting Data: Predicting Social Media-Influence in the NBA

Domino Data Lab

Knowing that the ultimate goal is to compare the social-media influence and power of NBA players, a great place to start is with the roster of the NBA players in the 2016–2017 season. You can add a Makefile command test that will run all of your notebooks by issuing. test: py.test --nbval notebooks/*.ipynb. In Figure 6.8,

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Using random effects models in prediction problems

The Unofficial Google Data Science Blog

Column "a" is an advertiser id, "b" is a web site, and "c" is the 'interaction' of columns "a" and "b". $y$ We compared the output of a random effects model to a penalized GLM solver with "Elastic Net" regularization (i.e. both L1 and L2 penalties; see [8]) which were tuned for test set accuracy (log likelihood). 1 1 1 1.10

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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

GloVe and word2vec differ in their underlying methodology: word2vec uses predictive models, while GloVe is count based. Note: A test set of 19,500 such analogies was developed by Tomas Mikolov and his colleagues in their 2013 word2vec paper. This test set is available at download.tensorflow.org/data/questions-words.txt.].

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Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

The need for interaction – complex decision making systems often rely on Human–Autonomy Teaming (HAT), where the outcome is produced by joint efforts of one or more humans and one or more autonomous agents. 2016) for an example of this technique (LIME). PDPs for the bicycle count prediction model (Molnar, 2009).

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