January 9, 2021
More often than not bagging covers only 90% to 95% of the dataset thus there are some 5% to 10% of the instances which don’t form part of any subset and hence no training is performed on them. Thus making these instances ideal for the purpose of cross validation. Such instances are called Out of Bag instances. When cross validation is performed using Out of Bag instances, it is termed as Out of Bag evaluation.
by : Monis Khan
Quick Summary:
More often than not bagging covers only 90% to 95% of the dataset thus there are some 5% to 10% of the instances which don’t form part of any subset and hence no training is performed on them. Thus making these instances ideal for the purpose of cross validation. Such instances are called Out of […]