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Weka is a machine learning set of tools that offers variate implementations of boosting algorithms like AdaBoost and LogitBoost R package GBM (Generalized Boosted Regression Models) implements extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine.
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. [1] Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression.
Explainable AI is a concept that describes AI’s ability to “explain” or justify any decisions ... a professor and director of the machine learning department at Carnegie Mellon ...
Accumulated local effects (ALE) is a machine learning interpretability method. Concepts ALE uses a conditional feature distribution as an input and generates ...
Applications of machine learning in earth sciences include geological mapping, gas leakage detection and geological features identification.Machine learning (ML) is a type of artificial intelligence (AI) that enables computer systems to classify, cluster, identify and analyze vast and complex sets of data while eliminating the need for explicit instructions and programming.
Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Q-learning at its simplest stores data in tables. This approach falters with increasing numbers of states/actions since the likelihood of the agent visiting a particular state and performing a particular action is ...
Himabindu "Hima" Lakkaraju is an Indian-American computer scientist who works on machine learning, artificial intelligence, algorithmic bias, and AI accountability.She is currently an Assistant Professor at the Harvard Business School and is also affiliated with the Department of Computer Science at Harvard University.
Examples include deep learning, probabilistic programming, and other machine learning and artificial intelligence applications. A computationally hard problem, which is key for some relevant machine learning tasks, is the estimation of averages over probabilistic models defined in terms of a Boltzmann distribution. Sampling from generic ...