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A simple flowchart representing a process for dealing with a non-functioning lamp.. A flowchart is a type of diagram that represents a workflow or process.A flowchart can also be defined as a diagrammatic representation of an algorithm, a step-by-step approach to solving a task.
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
A decision tree is a flowchart-like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes).
Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2. [36] Python consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community. [37] [38] [39] [40]
RAPTOR, the Rapid Algorithmic Prototyping Tool for Ordered Reasoning, [1] is a graphical authoring tool created by Martin C. Carlisle, Terry Wilson, Jeff Humphries and Jason Moore.
Flow chart language (FCL) is a simple imperative programming language designed for the purposes of explaining fundamental concepts of program analysis and specialization, in particular, partial evaluation. The language was first presented in 1989 by Carsten K. Gomard and Neil D. Jones. [1]
Competitive learning is a form of unsupervised learning in artificial neural networks, in which nodes compete for the right to respond to a subset of the input data. [ 1 ] [ 2 ] A variant of Hebbian learning , competitive learning works by increasing the specialization of each node in the network.
Bootstrap aggregating, also called bagging (from bootstrap aggregating) or bootstrapping, is a machine learning (ML) ensemble meta-algorithm designed to improve the stability and accuracy of ML classification and regression algorithms.
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