{"id":52337,"date":"2024-04-15T23:37:55","date_gmt":"2024-04-15T23:37:55","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=52337"},"modified":"2024-04-15T23:37:55","modified_gmt":"2024-04-15T23:37:55","slug":"the-svms-are-less-effective-when","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/the-svms-are-less-effective-when\/","title":{"rendered":"The SVM’s are less effective when:"},"content":{"rendered":"

[amp_mcq option1=”The data is linearly separable” option2=”The data is clean and ready to use” option3=”The data is noisy and contains overlapping points” option4=”None of these” correct=”option3″]<\/p>\n

The correct answer is C. The data is noisy and contains overlapping points.<\/p>\n

SVMs are a type of machine learning algorithm that can be used for classification and regression tasks. They work by finding a hyperplane in a high-dimensional space that separates the data points into two classes.<\/p>\n

SVMs are very effective when the data is linearly separable, meaning that there is a clear line that can be drawn between the two classes. However, when the data is noisy and contains overlapping points, it can be difficult for SVMs to find a good hyperplane. This is because the noise can make it difficult to determine which points belong to which class.<\/p>\n

In these cases, other machine learning algorithms, such as decision trees or support vector regression, may be more effective.<\/p>\n

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