{"id":16744,"date":"2020-02-06T13:46:18","date_gmt":"2020-02-06T18:46:18","guid":{"rendered":"https:\/\/bangla.salearningschool.com\/recent-posts\/?p=16744"},"modified":"2020-02-08T09:41:23","modified_gmt":"2020-02-08T14:41:23","slug":"misc-classifier-performance-and-model-selection","status":"publish","type":"post","link":"http:\/\/bangla.sitestree.com\/?p=16744","title":{"rendered":"Misc. : Classifier Performance and Model Selection"},"content":{"rendered":"

Cross Validation:<\/strong><\/p>\n

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en.wikipedia.org
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Cross<\/strong>–validation<\/strong> is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into. As such, the procedure is often called k-fold cross<\/strong>–validation<\/strong>.May 23, 2018<\/p>\n

machinelearningmastery.com \u203a k-fold-cross-validation<\/a><\/p>\n

A Gentle Introduction to k-fold Cross-Validation<\/a><\/h3>\n

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Model selection – Wikipedia<\/a><\/h3>\n

Model selection<\/strong> is the task of selecting a statistical model<\/a> from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments<\/a> such that the data collected<\/a> is well-suited to the problem of model selection. Given candidate models of similar predictive or explanatory power, the simplest model is most likely to be the best choice (Occam’s razor<\/a>).
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Model Selection<\/strong><\/em>
\nhttp:\/\/statweb.stanford.edu\/~jtaylo\/courses\/stats203\/notes\/selection.pdf<\/a><\/p>\n

Machine Learning Model Evaluation<\/h1>\n

“Holdout Cross-Validation<\/h2>\n