Dr. Nina Zumel recently published an excellent tutorial on a modeling technique she called impact coding. It is a pragmatic machine learning technique that has helped with more than one client project. Impact coding is a bridge from Naive Bayes (where each variable’s impact is added without regard to the known effects of any other [...] Related posts: Modeling Trick: Impact Coding of Categorical Variables with Many Levels The equivalence…
Related posts: Modeling Trick: Impact Coding of Categorical Variables with Many Levels Modeling Trick: the Signed Pseudo Logarithm Learn Logistic Regression (and beyond)
Related posts: A Personal Perspective on Machine Learning The differing perspectives of statistics and machine learning Kernel Methods and Support Vector Machines de-Mystified
Related posts: A Personal Perspective on Machine Learning Why you can not to use statistics to dispute magic Statistics to English Translation, Part 2b: Calculating Significance
Much of the data that the analyst uses exhibits extraordinary range. For example: incomes, company sizes, popularity of books and any “winner takes all process”; (see: Living in A Lognormal World). Tukey recommended the logarithm as an important “stabilizing transform” (a transform that brings data into a more usable form prior to generating exploratory statistics, [...] Related posts: Modeling Trick: Masked Variables Modeling Trick: Impact Coding of Categorical Variables with…
Introduction To implement many numeric simulations you need a sophisticated source of instances of random variables. The question is: how do you generate them? The literature is full of algorithms requiring random samples as inputs or drivers (conditional random fields, Bayesian network models, particle filters and so on). The literature is also full of competing [...] Related posts: What is a large enough random sample? Kernel Methods and Support Vector…
It is a subtle point that statistical modeling is different than model based science. However, empirical scientists seem to go out of their way to conflate the two before the public (as statistical modeling is easier to perform and model based science is more highly rewarded). It is often claimed that model based science is [...] Related posts: Statistics to English Translation, Part 2a: ’Significant’ Doesn’t Always Mean ’Important’ Statistics…
We give a simple explanation of the interrelated machine learning techniques called kernel methods and support vector machines. We hope to characterize and de-mystify some of the properties of these methods. To do this we work some examples and draw a few analogies. The familiar no matter how wonderful is not perceived as mystical. Goals [...] Related posts: Book Review: Ensemble Methods in Data Mining (Seni & Elder) Six Fundamental…
Nina Zumel recently gave a very clear explanation of logistic regression ( The Simpler Derivation of Logistic Regression ). In particular she called out the central role of log-odds ratios and demonstrated how the “deviance” (that mysterious quantity reported by fitting packages) is both a term in “the pseudo-R^2″ (so directly measures goodness of fit) [...] Related posts: The Simpler Derivation of Logistic Regression Learn Logistic Regression (and beyond) Large…
