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The correct answer is FALSE<\/strong>.<\/p>\n Gain ratio is a measure of the quality of a split in a decision tree. It is calculated as the ratio of the information gain of the split to the number of instances in the smaller partition. A higher gain ratio indicates a better split.<\/p>\n Gain ratio tends to prefer balanced splits, in which the number of instances in each partition is similar. This is because gain ratio is based on the information gain of the split, which is a measure of how much the split reduces the entropy of the data. Entropy is a measure of the uncertainty in the data, and a lower entropy indicates that the data is more certain. A split that reduces the entropy of the data by a large amount is considered to be a good split.<\/p>\n However, gain ratio can also prefer unbalanced splits if the information gain of the split is very high. This is because the gain ratio is not penalized for having a small number of instances in the smaller partition.<\/p>\n In general, gain ratio is a good measure of the quality of a split, but it is important to keep in mind that it can sometimes prefer unbalanced splits.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”TRUE” option2=”nan” option3=”nan” option4=”nan” correct=”option1″]<\/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-52259","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n