💡 Words with a Similar Meaning to "Multiclass classification"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| multi-label classification | In machine learning, multi-label classification or multi-output classification is a variant of the classification problem where multiple nonexclusive labels may be assigned to each instance. |
| multinomial logistic regression | In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes. |
| 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. |
| one-class classification | In machine learning, one-class classification, also known as unary classification or class-modelling, tries to identify objects of a specific class amongst all objects, by primarily learning from a training set containing only the objects of that class, although there exist variants of one-class classifiers where counter-examples are used to further refine the classification boundary. |
| sequence labeling | In machine learning, sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of a sequence of observed values. |
| linear classifier | In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features. |
| statistical classification | When classification is performed by a computer, statistical methods are normally used to develop the algorithm. |
| feature learning | In machine learning, feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations needed for feature detection or classification from raw data. |
| quadratic classifier | In statistics, a quadratic classifier is a statistical classifier that uses a quadratic decision surface to separate measurements of two or more classes of objects or events. |
| multinomial probit | In statistics and econometrics, the multinomial probit model is a generalization of the probit model used when there are several possible categories that the dependent variable can fall into. |
| multinomial test | the statistical test of the null hypothesis that the parameters of a multinomial distribution equal specified values; it is used for categorical data. |
| ensemble learning | In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. |
| margin classifier | In machine learning, a margin classifier is a type of classification model which is able to give an associated distance from the decision boundary for each data sample. |
| feature selection | In machine learning, feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. |
| parameterized complexity | In computer science, parameterized complexity is a branch of computational complexity theory that focuses on classifying computational problems according to their inherent difficulty with respect to multiple parameters of the input or output. |
| linear discriminant analysis | Linear discriminant analysis, normal discriminant analysis, or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics and other fields, to find a linear combination of features that characterizes or separates two or more classes of objects or events. |
| multiple comparisons problem | In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or estimates a subset of parameters selected based on the observed values. |
| latent class model | In statistics, a latent class model is a model for clustering multivariate discrete data. |
| platt scalingnoun | (machine learning) A technique for transforming the outputs of a classification model into a probability distribution over classes, based on a logistic regression model. |
| linear predictor function | In statistics and in machine learning, a linear predictor function is a linear function (linear combination) of a set of coefficients and explanatory variables (independent variables), whose value is used to predict the outcome of a dependent variable. |
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