one model is an opinion; a committee is an estimator. 𐃏 ensemble methods build many imperfect predictors and combine them, and the two great families attack opposite ends of the bias-variance decomposition: bagging averages low-bias, high-variance models to cancel their wobble; boosting stacks up high-bias, low-variance weak learners to build accuracy that none of them has alone.
Random-Forests
An Embedded Notebook
History
Abstract
The MNIST dataset (Modified National Institute of Standards and Technology) has been very influential in machine learning and computer vision. It is an easy and popular dataset that has been used since it’s inception in 1998 as a benchmark for Machine Learning Models. Historically it has enhanced the evolution of OCR (Optical Character Recognition) and assisted in the emergence of neural networks.
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Knowledge is a paradox. The more one understand, the more one realises the vastness of his ignorance.