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Proposals for model vulnerability and security

O'Reilly on Data

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. This is like a denial-of-service (DOS) attack on your model itself.

Modeling 227
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2019 US Open Predictions: Doubling Down on the Data

DataRobot Blog

We started with the result of every match (and set scores) for ATP and WTA tour matches from 2010 through 2018. Once we had built this prediction model , we could take the draw of any tournament and simulate the results 100,000 times to find out how often each player would win with that particular draw.

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Structural Evolutions in Data

O'Reilly on Data

Stage 2: Machine learning models Hadoop could kind of do ML, thanks to third-party tools. While data scientists were no longer handling Hadoop-sized workloads, they were trying to build predictive models on a different kind of “large” dataset: so-called “unstructured data.” And it was good.

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Using random effects models in prediction problems

The Unofficial Google Data Science Blog

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

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Exploring US Real Estate Values with Python

Domino Data Lab

Models are at the heart of data science. Data exploration is vital to model development and is particularly important at the start of any data science project. From 2010 to 2017, the median price of a single-family home in San Francisco has gone from approximately $775,000 to $1.5 Introduction. There is a good reason for that.