[amp_mcq option1=”Probabilistic model” option2=”Unsupervised model” option3=”Regression model” option4=”Decision tree model” correct=”option1″]<\/p>\n
The correct answer is A. Probabilistic model.<\/p>\n
A probabilistic model is a statistical model that assigns a probability to each possible outcome for a given input. This is in contrast to a deterministic model, which would always produce the same output for a given input. Probabilistic models are often used in machine learning to make predictions about future events. For example, a probabilistic model could be used to predict the probability of a customer making a purchase, or the probability of a loan defaulting.<\/p>\n
B. Unsupervised models are used to find patterns in data without any labeled training data. This is in contrast to supervised models, which are trained on labeled data. Unsupervised models are often used for tasks such as clustering and dimensionality reduction.<\/p>\n
C. Regression models are used to predict continuous values. This is in contrast to classification models, which are used to predict discrete values. Regression models are often used for tasks such as predicting house prices or predicting the stock market.<\/p>\n
D. Decision tree models are a type of supervised learning model that uses a tree-like structure to make predictions. Decision tree models are often used for tasks such as classification and regression.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”Probabilistic model” option2=”Unsupervised model” option3=”Regression model” option4=”Decision tree model” 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":[738],"tags":[],"class_list":["post-57234","post","type-post","status-publish","format-standard","hentry","category-data-science-miscellaneous","no-featured-image-padding"],"yoast_head":"\n