{"id":46021,"date":"2024-04-15T22:06:22","date_gmt":"2024-04-15T22:06:22","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=46021"},"modified":"2024-04-15T22:06:22","modified_gmt":"2024-04-15T22:06:22","slug":"which-of-the-following-can-be-one-of-the-steps-in-stacking-1-divide-the-training-data-into-k-folds-2-train-k-models-on-each-k-1-folds-and-get-the-out-of-fold-predictions-for-remaining-one-fold-3-d","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/which-of-the-following-can-be-one-of-the-steps-in-stacking-1-divide-the-training-data-into-k-folds-2-train-k-models-on-each-k-1-folds-and-get-the-out-of-fold-predictions-for-remaining-one-fold-3-d\/","title":{"rendered":"Which of the following can be one of the steps in stacking? 1. Divide the training data into k folds 2. Train k models on each k-1 folds and get the out of fold predictions for remaining one fold 3. Divide the test data set in “k” folds and get individual fold predictions by different algorithms"},"content":{"rendered":"

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

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

Stacking is a machine learning technique that combines the predictions of multiple models to produce a more accurate prediction. The steps involved in stacking are as follows:<\/p>\n

    \n
  1. The training data is divided into k folds.<\/li>\n
  2. k models are trained on each k-1 folds and the out of fold predictions are obtained for the remaining one fold.<\/li>\n
  3. The predictions from each model are then combined to produce a final prediction.<\/li>\n<\/ol>\n

    The first step, dividing the training data into k folds, is done to prevent overfitting. Overfitting occurs when a model learns the training data too well and is unable to generalize to new data. By dividing the data into k folds, each model is trained on a different subset of the data. This helps to prevent overfitting and ensures that each model learns the general trends in the data.<\/p>\n

    The second step, training k models on each k-1 folds and getting the out of fold predictions for the remaining one fold, is done to improve the accuracy of the predictions. Each model is trained on a different subset of the data, so each model will have a different perspective on the data. This helps to reduce the variance in the predictions and improve the accuracy of the final prediction.<\/p>\n

    The third step, combining the predictions from each model to produce a final prediction, is done to further improve the accuracy of the predictions. The predictions from each model are combined using a weighted average, where the weights are determined by the accuracy of each model. This helps to reduce the bias in the predictions and improve the accuracy of the final prediction.<\/p>\n

    Stacking is a powerful technique that can be used to improve the accuracy of machine learning models. It is often used in conjunction with other machine learning techniques, such as cross-validation, to further improve the accuracy of the predictions.<\/p>\n","protected":false},"excerpt":{"rendered":"

    [amp_mcq option1=”1 and 2″ option2=”2 and 3″ option3=”1 and 3″ option4=”all of above” correct=”option4″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-46021","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\nWhich of the following can be one of the steps in stacking? 1. Divide the training data into k folds 2. Train k models on each k-1 folds and get the out of fold predictions for remaining one fold 3. 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