[amp_mcq option1=”In sample error < out sample error\" option2=\"In sample error > out sample error” option3=”In sample error = out sample error” option4=”All of the mentioned” correct=”option3″]<\/p>\n
The correct answer is: C. In sample error = out sample error.<\/p>\n
In-sample error is the error that is calculated using the same data that was used to build the model. Out-of-sample error is the error that is calculated using data that was not used to build the model.<\/p>\n
In general, in-sample error is lower than out-of-sample error. This is because the model is trained on the in-sample data, so it is able to fit the data very well. However, when the model is used to make predictions on out-of-sample data, it is not able to fit the data as well, and so the error is higher.<\/p>\n
However, there are some cases where in-sample error can be higher than out-of-sample error. This can happen if the model is overfitted to the in-sample data. Overfitting occurs when the model learns the noise in the data instead of the underlying patterns. As a result, the model does not generalize well to out-of-sample data.<\/p>\n
In summary, in-sample error is typically lower than out-of-sample error. However, there are some cases where in-sample error can be higher than out-of-sample error. This can happen if the model is overfitted to the in-sample data.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”In sample error < out sample error\" option2=\"In sample error > out sample error” option3=”In sample error = out sample error” option4=”All of the mentioned” 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":[736],"tags":[],"class_list":["post-48292","post","type-post","status-publish","format-standard","hentry","category-machine-learning-algorithms","no-featured-image-padding"],"yoast_head":"\n