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Concept
Probabilistic classification
Résumé
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.g., the set of all documents, or the set of all images), while the class labels form a finite set Y defined prior to training.
Probabilistic classifiers generalize this notion of classifiers: instead of functions, they are conditional distributions , meaning that for a given , they assign probabilities to all (and these probabilities sum to one). "Hard" classification can then be done using the optimal decision rule
or, in English, the predicted class is that which has the highest probability.
Binary probabilistic classifiers are also called binary regression models in statistics. In econometrics, probabilistic classification in general is called discrete choice.
Some classification models, such as naive Bayes, logistic regression and multilayer perceptrons (when trained under an appropriate loss function) are naturally probabilistic. Other models such as support vector machines are not, but methods exist to turn them into probabilistic classifiers.
Some models, such as logistic regression, are conditionally trained: they optimize the conditional probability directly on a training set (see empirical risk minimization). Other classifiers, such as naive Bayes, are trained generatively: at training time, the class-conditional distribution and the class prior are found, and the conditional distribution is derived using Bayes' rule.
Not all classification models are naturally probabilistic, and some that are, notably naive Bayes classifiers, decision trees and boosting methods, produce distorted class probability distributions.
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Classification en classes multiples
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.
Dilemme biais-variance
En statistique et en apprentissage automatique, le dilemme (ou compromis) biais–variance est le problème de minimiser simultanément deux sources d'erreurs qui empêchent les algorithmes d'apprentissage supervisé de généraliser au-delà de leur échantillon d'apprentissage : Le biais est l'erreur provenant d’hypothèses erronées dans l'algorithme d'apprentissage. Un biais élevé peut être lié à un algorithme qui manque de relations pertinentes entre les données en entrée et les sorties prévues (sous-apprentissage).
Platt scaling
In machine learning, Platt scaling or Platt calibration is a way of transforming the outputs of a classification model into a probability distribution over classes. The method was invented by John Platt in the context of support vector machines, replacing an earlier method by Vapnik, but can be applied to other classification models. Platt scaling works by fitting a logistic regression model to a classifier's scores. Consider the problem of binary classification: for inputs x, we want to determine whether they belong to one of two classes, arbitrarily labeled +1 and −1.