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The correct answer is TRUE<\/strong>.<\/p>\n A multivariate split is a type of decision tree split that partitions tuples based on a combination of attributes rather than on a single attribute. This type of split is often used when the target variable is correlated with multiple attributes.<\/p>\n For example, consider a decision tree that is trying to predict whether a customer will churn. The tree might have a split on the attribute “age”. Customers who are younger than 30 might be more likely to churn, while customers who are older than 30 might be less likely to churn. However, this split might not be very accurate if there are other attributes that are correlated with churn, such as “income” or “marital status”.<\/p>\n In this case, a multivariate split might be more accurate. For example, the tree might split customers into two groups based on their age and income. Customers who are younger than 30 and have a low income might be more likely to churn, while customers who are older than 30 and have a high income might be less likely to churn.<\/p>\n Multivariate splits can be more complex to understand and implement than single-attribute splits. However, they can often be more accurate, especially when the target variable is correlated with multiple attributes.<\/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-51881","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n