{"id":58249,"date":"2024-04-16T01:16:12","date_gmt":"2024-04-16T01:16:12","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=58249"},"modified":"2024-04-16T01:16:12","modified_gmt":"2024-04-16T01:16:12","slug":"how-can-svm-be-classified","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/how-can-svm-be-classified\/","title":{"rendered":"How can SVM be classified?"},"content":{"rendered":"

[amp_mcq option1=”it is a model trained using unsupervised learning. it can be used for classification and regression.” option2=”it is a model trained using unsupervised learning. it can be used for classification but not for regression.” option3=”it is a model trained using supervised learning. it can be used for classification and regression.” option4=”t is a model trained using unsupervised learning. it can be used for classification but not for regression.” correct=”option3″]<\/p>\n

The correct answer is C. SVM is a model trained using supervised learning. It can be used for classification and regression.<\/p>\n

Supervised learning is a type of machine learning in which the model is trained on labeled data. This means that the model is given a set of data points, each of which has a known label. The model then learns to map the input data to the output labels.<\/p>\n

SVM is a type of supervised learning algorithm that can be used for both classification and regression. In classification, the model is trained to predict the class of an input data point. In regression, the model is trained to predict a continuous value for an input data point.<\/p>\n

SVM is a powerful machine learning algorithm that has been shown to be effective for a variety of tasks. It is often used in areas such as text classification, image classification, and object detection.<\/p>\n

Here is a brief explanation of each option:<\/p>\n