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Lecture

Statistical Modeling

Description

This lecture covers exercises related to statistical modeling, focusing on topics such as input data from Gibbs Ising, pruning in GCM, fairness in multiclass classification, backward compatibility measure, and simplification in teacher-student models. The slides also discuss various notes, meetings, seminars, and corrections from previous weeks.

Official source

Related concepts (23)

Multiclass classification

In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.

Statistical classification

In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or features.

Probabilistic classification

In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over a set of classes, rather than only outputting the most likely class that the observation should belong to. Probabilistic classifiers provide classification that can be useful in its own right or when combining classifiers into ensembles. Formally, an "ordinary" classifier is some rule, or function, that assigns to a sample x a class label ŷ: The samples come from some set X (e.

Naive Bayes classifier

In statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve high accuracy levels. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem.

Hangul

The Korean alphabet, known as Hangul (ˈhɑːnguːl ; ) in South Korea and Chosŏn'gŭl () in North Korea, is the modern official writing system for the Korean language. The letters for the five basic consonants reflect the shape of the speech organs used to pronounce them, and they are systematically modified to indicate phonetic features; similarly, the vowel letters are systematically modified for related sounds, making Hangul a featural writing system.

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