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What is Null and Residual deviance in logistic regression

@machinelearnbot

Above we can see that two deviances NULL and Residual. Here Value of NULL deviance can be read as 43,86 on 31 degrees of freedom and Residual deviance as 21.4 on 29 degrees of freedom. Deviance is a measure of goodness of fit of a model. Higher numbers always indicates bad fit.


Use H2O and data.table to build models on large data sets in R

@machinelearnbot

Last week, I wrote an introductory article on the package data.table. It was intended to provide you a head start and become familiar with its unique and short syntax. The next obvious step is to focus on modeling, which we will do in this post today. Atleast, I used to think of myself as a crippled R user when faced with large data sets. I would like to thank Matt Dowle again for this accomplishment. Algorithms like random forest (ntrees 1000) takes forever to run on my data set with 800,000 rows. I'm sure there are many R users who are trapped in a similar situation. To overcome this painstaking hurdle, I decided to write this post which demonstrates using the two most powerful packages i.e.


Generalized Beta Divergence

arXiv.org Machine Learning

This paper generalizes beta divergence beyond its classical form associated with power variance functions of Tweedie models. Generalized form is represented by a compact definite integral as a function of variance function of the exponential dispersion model. This compact integral form simplifies derivations of many properties such as scaling, translation and expectation of the beta divergence. Further, we show that beta divergence and (half of) the statistical deviance are equivalent measures.


Causal Inference via Kernel Deviance Measures

Neural Information Processing Systems

Discovering the causal structure among a set of variables is a fundamental problem in many areas of science. In this paper, we propose Kernel Conditional Deviance for Causal Inference (KCDC) a fully nonparametric causal discovery method based on purely observational data. From a novel interpretation of the notion of asymmetry between cause and effect, we derive a corresponding asymmetry measure using the framework of reproducing kernel Hilbert spaces. Based on this, we propose three decision rules for causal discovery. We demonstrate the wide applicability and robustness of our method across a range of diverse synthetic datasets.


Dear Prudence: The "Panoply of Deviance" Edition

Slate

Writer, commentator, and professor Roxane Gay joins Prudie this week for your questions! How do I avoid matching with my students on online dating apps? My boss won't stop telling me to smile. Should I report the places my ex-husband frequented for sex for human trafficking? I'm in love with a prude--how do I spice up our sex life?