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You're reading from  Clojure for Data Science

Product typeBook
Published inSep 2015
Reading LevelIntermediate
Publisher
ISBN-139781784397180
Edition1st Edition
Languages
Clojure
Concepts
Data Analysis
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Author (1)
Henry Garner
Henry Garner
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Henry Garner

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach. He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance. He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure. Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.
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Probability

We have encountered probability in several guises so far in this book: as p-values, confidence intervals, and most recently as the output of logistic regression where the result can be considered as the probability of the output class being positive. The probabilities we calculated for the kappa statistic were the result of adding up counts and dividing by totals. The probability of agreement, for example, was calculated as the number of times the model and the data agreed divided by the number of samples. This way of calculating probabilities is referred to as frequentist, because it is concerned with the rates at which things happen.

An output of 1.0 from logistic regression (pre-rounding) corresponds to the certainty that the input is in the positive class; an output of 0.0 corresponds to the certainty that the input isn't in the positive class. An output of 0.5 corresponds to complete uncertainty about the output class. For example, if ŷ = 0.7 the probability of y = 1 is 70...

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Clojure for Data Science
Published in: Sep 2015Publisher: ISBN-13: 9781784397180
© 2015 Packt Publishing Limited All Rights Reserved

Author (1)

author image
Henry Garner

Henry Garner is a graduate from the University of Oxford and an experienced developer, CTO, and coach. He started his technical career at Britain's largest telecoms provider, BT, working with a traditional data warehouse infrastructure. As a part of a small team for 3 years, he built sophisticated data models to derive insight from raw data and use web applications to present the results. These applications were used internally by senior executives and operatives to track both business and systems performance. He then went on to co-found Likely, a social media analytics start-up. As the CTO, he set the technical direction, leading to the introduction of an event-based append-only data pipeline modeled after the Lambda architecture. He adopted Clojure in 2011 and led a hybrid team of programmers and data scientists, building content recommendation engines based on collaborative filtering and clustering techniques. He developed a syllabus and copresented a series of evening classes from Likely's offices for professional developers who wanted to learn Clojure. Henry now works with growing businesses, consulting in both a development and technical leadership capacity. He presents regularly at seminars and Clojure meetups in and around London.
Read more about Henry Garner

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