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In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and usually a validation set) changes with the number of training iterations (epochs) or the amount of training data. [1]
A learning curve is a graphical representation of the relationship between how proficient people are at a task and the amount of experience they have. Proficiency (measured on the vertical axis) usually increases with increased experience (the horizontal axis), that is to say, the more someone, groups, companies or industries perform a task, the better their performance at the task.
In the adaptive control literature, the learning rate is commonly referred to as gain. [2] In setting a learning rate, there is a trade-off between the rate of convergence and overshooting. While the descent direction is usually determined from the gradient of the loss function, the learning rate determines how big a step is taken in that ...
Outline of machine learning; ... Precise Asymptotics and the Double Descent Curve". Communications on Pure and Applied Mathematics. 75 (4): 667–766. ...
A machine learning model is a type of mathematical model that, ... Receiver operating characteristic (ROC) along with the accompanying Area Under the ROC Curve (AUC ...
The performance of machine learning algorithms is commonly visualized by learning curve plots ... Rostamizadeh A., Talwakar A., (2018) Foundations of Machine learning ...
In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant . [ 1 ]
Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source), to label new data points with the desired outputs. The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources ...