Exam AWS Certified Machine Learning - Specialty topic 1 question 14 discussion

Answer is C. SVM sample use case is to put the dimensions into a higher hyperplane that can separates it. Seeing how separable it is, SVM can be used for it.

Support Vector Machine (SVM) with Non-Linear Kernel --> Non-linear Data Why? SVM is powerful for classification and works well even with small datasets. If the data has a non-linear decision boundary, using an SVM with a non-linear kernel (like RBF or polynomial) can improve accuracy. Works well in low-dimensional feature spaces (since we have only 2 features: age of account & transaction month). Optimal choice if the data has a non-linear decision boundary.

SVMs are particularly effective for binary classification tasks and can handle non-linear relationships between features1.

You can use a support vector machine (SVM) when your data has exactly two classes. An SVM classifies data by finding the best hyperplane that separates all data points of one class from those of the other class. The best hyperplane for an SVM means the one with the largest margin between the two classes. Margin means the maximal width of the slab parallel to the hyperplane that has no interior data points.

Well, C is the correct answer. This example is a classical one to use SVM.

SVM for RBF mode is the answer!

Answer is C

Textbook C

C. more reading for using non-linear kernel and separate samples with a hyperplane in a higher dimension space: https://medium.com/pursuitnotes/day-12-kernel-svm-non-linear-svm-5fdefe77836c

C seems right

answer is C

Agree. The answer is A. https://www.surveypractice.org/article/2715-using-support-vector-machines-for-survey-research

This is a good explanation of SVM https://uk.mathworks.com/help/stats/support-vector-machines-for-binary-classification.html