{"id":55853,"date":"2024-04-16T00:34:32","date_gmt":"2024-04-16T00:34:32","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=55853"},"modified":"2024-04-16T00:34:32","modified_gmt":"2024-04-16T00:34:32","slug":"which-of-the-following-are-advantages-of-stacking-1-more-robust-model-2-better-prediction-3-lower-time-of-execution","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/which-of-the-following-are-advantages-of-stacking-1-more-robust-model-2-better-prediction-3-lower-time-of-execution\/","title":{"rendered":"Which of the following are advantages of stacking? 1. More robust model 2. better prediction 3. Lower time of execution"},"content":{"rendered":"

[amp_mcq option1=”1 and 2″ option2=”2 and 3″ option3=”1 and 3″ option4=”all of the above” correct=”option4″]<\/p>\n

The correct answer is D. all of the above.<\/p>\n

Stacking is a machine learning technique that combines the predictions of multiple models to produce a more accurate prediction. It does this by creating a meta-model that learns how to combine the predictions of the base models. This can lead to more robust models that are less likely to overfit the training data. Additionally, stacking can improve the accuracy of predictions, especially when the base models are diverse. Finally, stacking can be used to reduce the time it takes to make predictions, as the meta-model can be trained once and then used to make predictions for new data without having to retrain the base models.<\/p>\n

Here is a brief explanation of each option:<\/p>\n