Logistic Regression (Episode 9) The Effective Data Scientist podcast

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Science Natural Sciences Alexander Schacht and Paolo Eusebi Alexander Schacht Paolo Eusebi Data Science Data Scientist

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Logistic regression (Episode 9)

1y ago 16:20

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AAC•Episode hjem

Indhold leveret af Alexander Schacht and Paolo Eusebi, Alexander Schacht, and Paolo Eusebi. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Alexander Schacht and Paolo Eusebi, Alexander Schacht, and Paolo Eusebi eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

Logistic regression is a beautiful tool for modeling a binary dependent variable, although many more complex extensions exist. In the show, we will speak about the generalized linear model family, logit and probit functions, interpretations, and practicalities.

Resources:

● McCullagh, Peter, and John A. Nelder. Generalized linear models. Routledge, 1983.

● Faraway, Julian J. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC, 2016. (http://https://julianfaraway.github.io/faraway/ELM/)

… continue reading

24 episoder

#Science #Natural Sciences #Alexander Schacht and Paolo Eusebi #Alexander Schacht #Paolo Eusebi #Data Science #Data Scientist

Artwork

Logistic regression (Episode 9)

The Effective Data Scientist

published 1y ago

Del

AAC•Episode hjem

Indhold leveret af Alexander Schacht and Paolo Eusebi, Alexander Schacht, and Paolo Eusebi. Alt podcastindhold inklusive episoder, grafik og podcastbeskrivelser uploades og leveres direkte af Alexander Schacht and Paolo Eusebi, Alexander Schacht, and Paolo Eusebi eller deres podcastplatformspartner. Hvis du mener, at nogen bruger dit ophavsretligt beskyttede værk uden din tilladelse, kan du følge processen beskrevet her https://da.player.fm/legal.

Logistic regression is a beautiful tool for modeling a binary dependent variable, although many more complex extensions exist. In the show, we will speak about the generalized linear model family, logit and probit functions, interpretations, and practicalities.

Resources:

● McCullagh, Peter, and John A. Nelder. Generalized linear models. Routledge, 1983.

● Faraway, Julian J. Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. Chapman and Hall/CRC, 2016. (http://https://julianfaraway.github.io/faraway/ELM/)

… continue reading

24 episoder

#Science #Natural Sciences #Alexander Schacht and Paolo Eusebi #Alexander Schacht #Paolo Eusebi #Data Science #Data Scientist

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