[amp_mcq option1=”To collect more data” option2=”To build predictive models” option3=”To assess a model’s performance on unseen data by splitting the data into multiple subsets” option4=”To create visualizations” correct=”option3″]<\/p>\n
The correct answer is C. To assess a model’s performance on unseen data by splitting the data into multiple subsets.<\/p>\n
Cross-validation is a technique used in machine learning to evaluate the performance of a model on unseen data. It does this by splitting the data into multiple subsets, called folds. The model is then trained on one fold and evaluated on another fold. This process is repeated for each fold, and the results are averaged to get an estimate of the model’s performance.<\/p>\n
Cross-validation is important because it allows us to assess the performance of a model on data that it has not seen before. This is important because it gives us a more accurate estimate of how well the model will perform on new data.<\/p>\n
Option A is incorrect because cross-validation does not collect more data. It uses the data that is already available to evaluate the model’s performance.<\/p>\n
Option B is incorrect because cross-validation is not used to build predictive models. It is used to evaluate the performance of models that have already been built.<\/p>\n
Option D is incorrect because cross-validation is not used to create visualizations. It is used to evaluate the performance of models.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”To collect more data” option2=”To build predictive models” option3=”To assess a model’s performance on unseen data by splitting the data into multiple subsets” option4=”To create visualizations” correct=”option3″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[738],"tags":[],"class_list":["post-55239","post","type-post","status-publish","format-standard","hentry","category-data-science-miscellaneous","no-featured-image-padding"],"yoast_head":"\n