Linear Models
Linear models are very useful for baseline models.
3 Logistic Regression
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Category: { Machine Learning }
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References:
- Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Science & Business Media; 2013. pp. 567–567. Available: https://play.google.com/store/books/details?id=yPfZBwAAQBAJ
- Friedman J, Hastie T, Tibshirani R. Additive Logistic Regression. The Annals of Statistics. 2000. pp. 337–374. doi:10.1214/aos/1016218223
- Mehta P, Wang C-H, Day AGR, Richardson C, Bukov M, Fisher CK, et al. A high-bias, low-variance introduction to Machine Learning for physicists. Phys Rep. 2019;810: 1–124. doi:10.1016/j.physrep.2019.03.001
Summary: logistics regression is a simple model for classification
Pages: 3
2 Poisson Regression
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Category: { Machine Learning }
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References:
- Fox J. Applied Regression Analysis and Generalized Linear Models. SAGE Publications; 2015. Available: https://play.google.com/store/books/details?id=cjB3BwAAQBAJ
- Rodríguez G, editor. Poisson Models for CountData. Generalized Linear Models. Available: https://data.princeton.edu/wws509/notes
- Chapter 19: Logistic and Poisson Regression by Marie Chesaniuk
- Poisson regression and non-normal loss (sklearn documentation)
- Beyond Multiple Linear Regression, Chapter 4 Poisson Regression
Summary: Poisson regression is a generalized linear model for count data.
To model a dataset that is generated from a [[Poisson distribution]] Poisson Process , we only need to model the mean as it is the only parameters. The simplest model we can have for some given features is a linear model. However, for count data, the effects of the predictors are often multiplicative. The next simplest model we can have is
The makes sure that the mean is positive as this is required for count data.
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