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OpenML: [494] Web platform with Python, R, Java, and other APIs for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms. PMLB: [495] A large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms ...
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. [1] Such algorithms function by making data-driven predictions or decisions, [2] through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data ...
The environmental data are most often climate data (e.g. temperature, precipitation), but can include other variables such as soil type, water depth, and land cover. SDMs are used in several research areas in conservation biology , ecology and evolution .
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
Download QR code; Print/export Download as PDF; Printable version; In other projects Wikidata item; Appearance. ... Pages in category "Datasets in machine learning"
In some applications such as automatic calibration or machine learning, the NSE lower limit of (−∞) creates problems. To eliminate this problem and re-scale the NSE to lie solely within the range of {0,1} normalization, use the following equation that yields a Normalized Nash–Sutcliffe Efficiency (NNSE) [6] [7]
Deep learning is a type of machine learning that runs inputs through biologically inspired artificial neural networks for all of these types of learning. [ 48 ] Computational learning theory can assess learners by computational complexity , by sample complexity (how much data is required), or by other notions of optimization .
Water retention curve is the relationship between the water content, θ, and the soil water potential, ψ. The soil moisture curve is characteristic for different types of soil, and is also called the soil moisture characteristic. It is used to predict the soil water storage, water supply to the plants (field capacity) and soil aggregate stability.