Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [generalization]

19 questions
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Is there a multivariate joint Amoroso distribution?

The Amoroso distribution is a remarkable feat of abstraction as it exactly or asymptotically generalizes dozens of named probability distributions. Is there a published/pre-published treatment of multivariate Amoroso distributions? Either the…
DifferentialPleiometry
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What are the tradeoffs of using the generalized $f$-median?

The generalized f-mean is a generalization of multiple estimators, and even generalizes the generalized mean. For some invertible function $f$, and $k$-dimensional vector, it is given as: $$M_f(\vec{x}) \triangleq f^{-1} \left( \frac{1}{n}…
DifferentialPleiometry
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Mean of Generalization of the Dirichlet Distribution

I know that if $X_{1},X_{2},...X_{n}$ are independent $\mathrm{Gamma}(\alpha_{i},\theta)$ - distributed variables (notice they all have the same scale parameter $\theta$) and $Y_{i}=\frac{X_{i}}{\sum_{j=1}^{n}X_{j}}$ then…
bbecon
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What does one imply by the term "overgeneralization" in machine learning?

I know overfitting and underfitting in machine learning context, and what generalisation means as well. But, recently I was introduced to an uncommon terminology "overgeneralization" in context of fitting. What should this term relate to?…
Umang Agarwal
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Cannot achieve generalization of machine learning model

I'm working on a balanced, binary classification problem in a time-series (financial) dataset. I am using K-fold cross validation that is adapted for time-series (so that I'm never using future data to predict past data). I have tried many…
Vladimir Belik
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Statistics terminology: $n$-way and $m$-sample

In statistics, I see certain things described by "$n$-way" or "$m$-sample." For example, there is "$n$-way" ANOVA for any $n$ and "$m$-sample" t-tests for $m=1,2$. I want to get a handle on what these descriptors mean in general. It seems to me like…
user179309
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Overfitting, generalization, data augmentation, regularization, how do they relate to each other? How to measure?

Recent work such as Deep Double Descent shows that overfitting is not really a problem with large models, even without any data augmentation or regularization (L2 weight norm, dropout or so). Edit: Ok, maybe this is a wrong conclusion from this…
Albert
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External loss functions for Spectral/Density-based clustering

In this article, Abou-Mustafa and Schuurmans proposed a method that makes it easy to decide what unsupervised learning algorithm generalizes 'better' to the entire dataset. In particular, this needs some external loss function l to measure…
drommedaris
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Metafor Package: How to conduct Meta Regression with reliability generalization

How to conduct meta regression in "metafor" after I got I2 heterogenity 94%. My study reliability generalization alpha Cronbach, with continuous and categorical moderator variable. Thanks all.
Wahyu Syahputra
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SVM Model: What's a healthy number of support vectors?

For a SVM model what is a healthy number of support vectors? or more precisely what's a good ratio of number of support vectors to the total number of training samples, 10%, 20%, 30%, 50% ... 80%? Is there a general consensus on this? By healthy I…
SkyWalker
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Does up-sampling lead to lots of false positives in production?

Say we have a dataset with a binary outcome variable that takes the positive case (outcome = 1) roughly 20% of the time. Often, we would modify the training set by down-sampling the 0's such that the training set has something like a 50/50 split in…
AmeySMahajan
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Clarification of line in proof of consistency theorem (Vapnik)

In Vapnik's Statistical Learning Theory (1998 edition) on pages 89-92, he proves a "key theorem of learning theory" that states the conditions for when: "the following two statements are equivalent: For the given distribution function F(z), the…
kushal3553
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Consequences of paired healthy and diseased samples in machine learning

Consider a set-up in which we are using machine learning to classify between healthy and diseased samples. Obtaining the data requires some invasive procedure - therefore all the healthy samples come from the same patients as the diseased samples,…
N Blake
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Rule of thumb for removing / keeping attribute based on occurrence frequencies among training observations?

I have a training dataset expressed with binary values, where 1 indicates an attribute is used in an observation, and 0 indicates it is not. I was wondering if I should remove an attribute from the training vector if it is used by only small number…
GabiX
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Is it possible to know whether a linear SVM is overfitting from the features' weight and value distribution in training?

I have a text sentiment classification model trained using linear SVM on 2500 training instances with around 14000 features(word), every sample is represented as binary vector with 1 indicate presence of a word and 0 indicate the absence of the word…
GabiX
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