Remove 2006 Remove Measurement Remove Modeling Remove Statistics
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Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

Selection and aggregation of forecasts from an ensemble of models to produce a final forecast. Calendaring was therefore an explicit feature of models within our framework, and we made considerable investment in maintaining detailed regional calendars. Adjustments for effects: holiday, seasonality, and day-of-week effects.

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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

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What’s the Difference: Quantitative vs Qualitative Data

Alation

From product development to customer satisfaction, nearly every aspect of a business uses data and analytics to measure success and define strategies. Measures of central tendency. Traditional business analysis uses numerical methods to paint a picture, often through numerical methods, like statistics. Percentages.

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A Big Data Imperative: Driving Big Action

Occam's Razor

Clickstream + qualitative data + rigorous statistical analysis of outcomes + deep mining of data from competitive intelligence sources + rapid experiments + more. But that can only happen if there is a model that defines the purpose of your sweet big data adventures. " That is the title of my post from June 2006.

Big Data 127
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Building a Named Entity Recognition model using a BiLSTM-CRF network

Domino Data Lab

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. How to build a statistical Named Entity Recognition (NER) model.

Modeling 111
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Misleading Statistics Examples – Discover The Potential For Misuse of Statistics & Data In The Digital Age

datapine

1) What Is A Misleading Statistic? 2) Are Statistics Reliable? 3) Misleading Statistics Examples In Real Life. 4) How Can Statistics Be Misleading. 5) How To Avoid & Identify The Misuse Of Statistics? If all this is true, what is the problem with statistics? What Is A Misleading Statistic?