Probabilistic classificationIn 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.
Attention (machine learning)Machine learning-based attention is a mechanism mimicking cognitive attention. It calculates "soft" weights for each word, more precisely for its embedding, in the context window. It can do it either in parallel (such as in transformers) or sequentially (such as recursive neural networks). "Soft" weights can change during each runtime, in contrast to "hard" weights, which are (pre-)trained and fine-tuned and remain frozen afterwards. Multiple attention heads are used in transformer-based large language models.
Data wranglingData wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. The goal of data wrangling is to assure quality and useful data. Data analysts typically spend the majority of their time in the process of data wrangling compared to the actual analysis of the data.
Defence mechanismIn psychoanalytic theory, a defence mechanism (American English: defense mechanism) is an unconscious psychological operation that functions to protect a person from anxiety-producing thoughts and feelings related to internal conflicts and outer stressors. Defence mechanisms (Abwehrmechanismen) are unconscious psychological processes employed to defend against feelings of anxiety and unacceptable impulses at the level of consciousness.
Medical classificationA medical classification is used to transform descriptions of medical diagnoses or procedures into standardized statistical code in a process known as clinical coding. Diagnosis classifications list diagnosis codes, which are used to track diseases and other health conditions, inclusive of chronic diseases such as diabetes mellitus and heart disease, and infectious diseases such as norovirus, the flu, and athlete's foot. Procedure classifications list procedure code, which are used to capture interventional data.
Euclidean spaceEuclidean space is the fundamental space of geometry, intended to represent physical space. Originally, that is, in Euclid's Elements, it was the three-dimensional space of Euclidean geometry, but in modern mathematics there are Euclidean spaces of any positive integer dimension n, which are called Euclidean n-spaces when one wants to specify their dimension. For n equal to one or two, they are commonly called respectively Euclidean lines and Euclidean planes.
Support vector machineIn machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laboratories by Vladimir Vapnik with colleagues (Boser et al., 1992, Guyon et al., 1993, Cortes and Vapnik, 1995, Vapnik et al., 1997) SVMs are one of the most robust prediction methods, being based on statistical learning frameworks or VC theory proposed by Vapnik (1982, 1995) and Chervonenkis (1974).
K-d treeIn computer science, a k-d tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k-dimensional space. k-d trees are a useful data structure for several applications, such as searches involving a multidimensional search key (e.g. range searches and nearest neighbor searches) and creating point clouds. k-d trees are a special case of binary space partitioning trees. The k-d tree is a binary tree in which every node is a k-dimensional point.
Self-harmSelf-harm is intentional behavior that is considered harmful to oneself. This is most commonly regarded as direct injury of one's own skin tissues usually without a suicidal intention. Other terms such as cutting, self-injury, and self-mutilation have been used for any self-harming behavior regardless of suicidal intent. The most common form of self-harm is using a sharp object to cut the skin. Other forms include scratching, hitting, or burning body parts.
International Classification of DiseasesThe International Classification of Diseases (ICD) is a globally used diagnostic tool for epidemiology, health management and clinical purposes. The ICD is maintained by the World Health Organization (WHO), which is the directing and coordinating authority for health within the United Nations System. The ICD is originally designed as a health care classification system, providing a system of diagnostic codes for classifying diseases, including nuanced classifications of a wide variety of signs, symptoms, abnormal findings, complaints, social circumstances, and external causes of injury or disease.
Data scienceData science is an interdisciplinary academic field that uses statistics, scientific computing, scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured, and unstructured data. Data science also integrates domain knowledge from the underlying application domain (e.g., natural sciences, information technology, and medicine). Data science is multifaceted and can be described as a science, a research paradigm, a research method, a discipline, a workflow, and a profession.
Data PreprocessingData preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues. Analyzing data that has not been carefully screened for such problems can produce misleading results.
