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Hands-On Automated Machine Learning
book

Hands-On Automated Machine Learning

by Sibanjan Das, Umit Mert Cakmak
April 2018
Beginner to intermediate content levelBeginner to intermediate
282 pages
6h 52m
English
Packt Publishing
Content preview from Hands-On Automated Machine Learning

Cross-validation

Cross-validation is a way to evaluate the accuracy of a model on a dataset that was not used for training, that is, a sample of data that is unknown to trained models. This ensures generalization of a model on independent datasets when deployed in a production environment. One of the methods is dividing the dataset into two sets—train and test sets. We demonstrated this method in our previous examples.

Another popular and more robust method is a k-fold cross-validation approach, where a dataset is partitioned into k subsamples of equal sizes. Where k is a non-zero positive integer. During the training phase, k-1 samples are used to train the model and the remaining one sample is used to test the model. This process is repeated ...

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Publisher Resources

ISBN: 9781788629898Supplemental Content