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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 ...
This is a list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of images or videos for tasks such as object detection, facial recognition, and multi-label classification.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Digital soil mapping (DSM) in soil science, also referred to as predictive soil mapping [1] or pedometric mapping, is the computer-assisted production of digital maps of soil types and soil properties. Soil mapping, in general, involves the creation and population of spatial soil information by the use of field and laboratory observational ...
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 .
The Water Erosion Prediction Project (WEPP) model is a physically based erosion simulation model built on the fundamentals of hydrology, plant science, hydraulics, and erosion mechanics. [ 1 ] [ 2 ] The model was developed by an interagency team of scientists to replace the Universal Soil Loss Equation (USLE) and has been widely used in the ...
Structured prediction or structured output learning is an umbrella term for supervised machine learning techniques that involves predicting structured objects, rather than discrete or real values. [ 1 ]
FoodData Central is USDA's integrated data system that contains five types of data containing information on food and nutrient profiles: [2] Standard Reference, using earlier approaches to determining nutrient profiles of foods in the marketplace, provides a comprehensive list of values for nutrients and food components that are derived from calculations and analyses.