Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [statistical-learning]

SL refers to the statistical perspective on machine learning.

Statistical learning refers to the statistical perspective on machine learning. It does not cover all ML topics; it focuses primarily on predictive modeling (regression and classification).

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How does a Support Vector Machine (SVM) work?

How does a Support Vector Machine (SVM) work, and what differentiates it from other linear classifiers, such as the Linear Perceptron, Linear Discriminant Analysis, or Logistic Regression? * (* I'm thinking in terms of the underlying motivations for…
tdc
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Supervised clustering or classification?

The second question is that I found in a discussion somewhere on the web talking about "supervised clustering", as far as I know, clustering is unsupervised, so what is exactly the meaning behind "supervised clustering" ? What is the difference with…
shn
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What is data blending?

This term appears frequently in the method-related threads. Is blending a specific method in data-mining and statistical learning? I cannot get a relevant result from google. It seems blending is mixing up outcomes from many models and resulting in…
TomHall
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How can a vector of variables represent a hyperplane?

I am reading Elements of Statistical Learning and on page 12 (section 2.3) a linear model is notated as: $$\widehat{Y} = X^{T} \widehat{\beta}$$ ...where $X^{T}$ is the transpose of a column vector of the predictors / independent variables / inputs.…
Scott
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Why should we discuss convergence behaviors of different estimators in different topologies?

In the first chapter of the book Algebraic Geometry and Statistical Learning Theory which talks about the convergence of estimations in different functional space, it mentions that the Bayesian estimation corresponds to the Schwartz distribution…
ziyuang
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Is Random Forest a good option for unbalanced data Classification?

Despite the resembling and other increasing data variability approaches, can the random forest "as an algorithm" be considered a good option for the unbalanced data classification?
mhdella
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Difference between Bias and Error?

In statistics, what is the difference between Bias and Error? You can say, Bias is a type of error? or Bias is an error with some tendency?
Darwin PC
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Clarification in information geometry

This question is concerned with the paper Differential Geometry of Curved Exponential Families-Curvatures and Information Loss by Amari. The text goes as follows. Let $S^n=\{p_{\theta}\}$ be an $n$-dimensional manifold of probability distributions…
Ashok
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Confused by Derivation of Regression Function

I just got a copy of The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman. In chapter 2 (Overview of Supervised Learning) section 4 (Statistical Decision Theory), he gives a derivation of the regression function. Let $X \in…
Orangutango
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Logistic regression for data from Poisson distributions

From some machine learning notes talking about some discriminative classification methods, in particular logistic regression, where y is the class label (0 or 1) and x is the data, it is said that: if $x|y = 0 \sim \mathrm{Poisson}(λ_0)$, and $x|y…
sambajetson
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How are piecewise cubic spline bases constructed?

There are words from the The Elements of Statistical Learning on page 119: It is not hard to show that the following basis represents a cubic spline with knots at $\xi_1$ and $\xi_2$: $h_1(X)=1$…
jerry_sjtu
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Extending 2-class models to multi-class problems

This paper on Adaboost gives some suggestions and code (page 17) for extending 2-class models to K-class problems. I would like to generalize this code, such that I can easily plug in different 2-class models and compare the results. Because most…
Zach
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Statistical learning theory VS computational learning theory?

What relations and differences are between statistical learning theory and computational learning theory? Are they about the same topic? Solve the same problems, and use the same methods? For example, the former says it is the theory of prediction…
Tim
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Fat-shattering dimension

A set of points $X = \{x\}$ is $\gamma$-shattered by a set of functions $\mathcal{F}$ if there are real numbers $r_x$ indexed by $x$ such that for any binary vector $b$ defining labeling of points from $X$ we can find a function $f \in \mathcal{F}$…
Leo
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Understanding the different formulations for SVM

I've been working with kernlab for more than a year now, but I always stickied to the vanilla cost (C-svc) formulation for classification. Of course, kernlab includes some other formulations. In the manual, some classification formulations are…
Firebug
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