ML Tasks

aka Query Learning

A machine learning task focused on methods that interactively query a user or another information source to label new data points with the d…

A supervised learning focused on a rule-based approach for discovering interesting relations between variables in large databases.

aka Block Clustering, Co-clustering…

A machine learning task focused on methods that simultaneously cluster the rows and columns of a matrix to identify submatrices with coheren…

A classification focused on methods that classify elements into two groups based on a classification rule.

aka Casaul Bayesian Network, Casaul Graph…

A probabilistic graphical model used to encode assumptions about the data-generating process.

A supervised learning focused on methods that distinguish and distribute kinds of "things" into different groups.

aka Cluster analysis

A machine learning task focused on methods that group a set of objects such that objects in the same group are more similar to each other th…

A machine learning task focused on methods that replace missing data with substituted values.

A classification that uses a tree-like model of decisions and their possible consequences including chance event outcomes resource costs and…

aka Dimension Reduction

An unsupervised learning focused on the process of transforming data from a high-dimensional space into a lower-dimensional space while reta…

A machine learning task focused on methods that use multiple learning algorithms to achieve better predictive performance than any of the co…

aka FEM

A regression analysis in which the model parameters are fixed or non-random quantities.

aka GLM

A regression analysis that relates the linear model to the response variable via a link function and allowing the variance of each measureme…

A classification focused on methods that group things according to a hierarchy.

aka HCL

A clustering that builds a hierarchy of clusters.

aka K-NN, KNN

A machine learning task that groups objects by a plurality vote of its neighbors, assigning each object to the class most common among its k…

aka K-NN Classification, KNN Classification

A classification and clustering that classifies objects by a plurality vote of its neighbors, assigning each object to the class most common…

aka K-NN Regression, KNN Regression

A regression analysis that assigns the average of the values of k nearest neighbors to objects.

A regression analysis that performs both variable selection and regularization to enhance prediction accuracy and interpretability.

A regression analysis which approximates the solution of overdetermined systems by minimizing the sum of the squares of the residuals.

A regression analysis that is a linear approach for modeling the relationship between a scalar response and one or more explanatory variable…

A regression analysis that estimates the probability of an event occurring by modeling the log-odds of the event as a linear combination of…

aka Machine Learning

A process intended to build methods that learn from data.

A dimensionality reduction method based on the assumption that observed data lie on a low-dimensional manifold embedded in a higher-dimensio…

A machine learning task that automatically learns from metadata about machine learning experiments.

aka Multinomial Classification

A classification focused on methods that classify instances into one of three or more classes.

aka MDS

A dimensionality reduction method that translates information about the pairwise distances among a set of objects or individuals into a conf…

A machine learning task that uses multiple modalities of data such as text audio and images to improve learning outcomes.

aka NLP

A machine learning task focused on the interactions between computers and human language including programming computers to process and anal…

aka N2V

A machine learning task designed to learn continuous feature representations for nodes in a graph by optimizing a neighborhood-preserving ob…

aka N2V-CBOW, CBOW

A node2vec that predicts the current node from a window of surrounding context nodes, with the order of context nodes not influencing predic…

aka N2V-SkipGram, SkipGram

A node2vec that uses the current node to predict the surrounding window of context nodes, weighing nearby context nodes more heavily than di…

aka PCA

A dimensionality reduction method for analyzing large datasets with high-dimensional features per observation increasing data interpretabili…

aka Graphical Model, PGM…

A machine learning task in which a graph expresses the conditional dependence structure between random variables.

A probabilistic graphical model that uses statistical techniques to analyze the words in each text to discover common themes their connectio…

A regression analysis for survival analysis where the unique effect of a unit increase in a covariate is multiplicative with respect to the…

aka REM

A regression analysis where the model parameters are random variables.

An ensemble learning method for classification regression and other tasks that constructs a multitude of decision trees during training.

aka Regression analysis, Regression model

A set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables.

A machine learning task focused on methods that do not require labeled input/output pairs or explicit correction of sub-optimal actions focu…

A regression analysis that estimates the coefficients of multiple regression models in scenarios where the independent variables are highly…

A machine learning task that is intermediate between supervised and unsupervised learning and predicts parts of the input data from other ob…

A regression analysis used to model spatial relationships.

aka Supervised Block Clustering, Supervised Co-clustering…

A biclustering task focused on methods that simultaneously cluster the rows and columns of a labeled matrix considering data labels to enhan…

aka Supervised Cluster Analysis

A clustering focused on methods that group labeled objects such that objects in the same group have similar labels relative to those in othe…

A machine learning task focused on methods that learn a function mapping input to output based on example input-output pairs.

A machine learning task focused on methods for analyzing the expected duration of time until one or more events occur such as death in biolo…

aka t-SNE, tSNE

A dimensionality reduction for visualizing high-dimensional data by giving each datapoint a location in a two or three-dimensional map.

A machine learning task focused on methods for analyzing time series data to extract meaningful statistics and characteristics.

A machine learning task focused on methods that predict future values based on previously observed values.

A machine learning task focused on methods that reuse or transfer information from previously learned tasks to facilitate the learning of ne…

aka Unsupervised Block Clustering, Unsupervised Co-clustering…

A biclustering task focused on methods that simultaneously cluster the rows and columns of an unlabeled input matrix to identify submatrices…

aka Unsupervised Cluster Analysis

A clustering focused on methods that group a set of unlabeled objects such that objects in the same group are more similar to each other tha…

A machine learning task focused on algorithms that learn patterns from unlabeled data.

aka W2V

A machine learning task that generates distributed representations of words by training a shallow neural network model, which aims to predic…

aka W2V-CBOW, CBOW

A word2vec that predicts the current word from a window of surrounding context words, ignoring the order of context words.

aka W2V-SkipGram, SkipGram

A word2vec that predicts surrounding context words from the current word, giving more weight to nearby context words than distant ones.