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

Domino Data Lab

Model distillation – this approach builds a separate explainable model that mimics the input-output behaviour of the deep network. Because this separate model is essentially a white-box, it can be used for extraction of rules that explain the decisions behind the ANN. 2016) for an example of this technique (LIME).

Modeling 139
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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

Random Effect Models We will start by describing a Gaussian regression model with known residual variance $sigma_j^2$ of the $j$th training record's response, $y_j$. Often our data can be stored or visualized as a table like the one shown below. Compact approximations to bayesian predictive distributions." ICML, (2005).

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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.].