[amp_mcq option1=”in pre-pruning a tree is pruned by halting its construction early” option2=”a pruning set of class labelled tuples is used to estimate cost complexity” option3=”the best pruned tree is the one that minimizes the number of encoding bits” option4=”all of the above” correct=”option4″]<\/p>\n
The correct answer is D. All of the above.<\/p>\n
In pre-pruning, a tree is pruned by halting its construction early. This is done by estimating the cost complexity of the tree and then pruning any branches that do not contribute significantly to the overall accuracy of the tree.<\/p>\n
A pruning set of class labelled tuples is used to estimate cost complexity. This set is used to estimate the number of errors that would be made by the tree if it were to be used to classify new data.<\/p>\n
The best pruned tree is the one that minimizes the number of encoding bits. This is because the number of encoding bits is a measure of the complexity of the tree. The smaller the number of encoding bits, the simpler the tree and the less likely it is to overfit the training data.<\/p>\n
Here is a more detailed explanation of each option:<\/p>\n
In pre-pruning, a tree is pruned by halting its construction early. This is done by estimating the cost complexity of the tree and then pruning any branches that do not contribute significantly to the overall accuracy of the tree.<\/p>\n
The cost complexity of a tree is a measure of the number of errors that the tree is likely to make on new data. It is estimated by using a pruning set of class labelled tuples. The pruning set is used to estimate the number of errors that would be made by the tree if it were to be used to classify new data.<\/p>\n
The branches of a tree that contribute the least to the overall accuracy of the tree are the ones that are most likely to be pruned. This is because these branches are the ones that are most likely to lead to errors on new data.<\/p>\n
A pruning set of class labelled tuples is used to estimate cost complexity. This set is used to estimate the number of errors that would be made by the tree if it were to be used to classify new data.<\/p>\n
The pruning set is a set of data points that are used to estimate the cost complexity of the tree. The data points in the pruning set are labelled with their class labels. The cost complexity of the tree is estimated by using the data points in the pruning set to calculate the number of errors that the tree would make on new data.<\/p>\n
The best pruned tree is the one that minimizes the number of encoding bits. This is because the number of encoding bits is a measure of the complexity of the tree. The smaller the number of encoding bits, the simpler the tree and the less likely it is to overfit the training data.<\/p>\n
The number of encoding bits is a measure of the complexity of a tree. It is calculated by counting the number of nodes in the tree. The fewer the number of nodes in the tree, the simpler the tree and the less likely it is to overfit the training data.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”in pre-pruning a tree is pruned by halting its construction early” option2=”a pruning set of class labelled tuples is used to estimate cost complexity” option3=”the best pruned tree is the one that minimizes the number of encoding bits” option4=”all of the 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-43073","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n