SVMs are effective for classification with non-linear decision boundaries because they:Introduce non-linear activation functionsUtilize feature engineering techniquesMap the input data into a higher-dimensional spaceApply ensemble learning algorithms

What is a non-linear support vector machine?Question 10Answera.A support vector machine that is sensitive to the scale of the input variablesb.A support vector machine that uses a non-linear kernel functionc.A support vector machine that is sensitive to the presence of outliersd.A support vector machine that uses a linear kernel function

What is the main objective of support vector machines (SVM)?Review LaterTo model complex decision boundaries in high-dimensional data.To handle nonlinear relationships between variables.To maximize the margin between data points of different classes.To introduce nonlinearity to neural networks.

Question 5Support vector machines can be extended to work with nonlinear classification boundaries by ___________________.1 pointmodifying the standard sigmoid functionprojecting the feature space onto a lower dimensional spaceincorporating polynomial regressionusing the kernel trick

Which kernel function is commonly used in a Support Vector Machine Classifier when dealing with non-linearly separable data?Review LaterLinear KernelPolynomial KernelRadial Basis Function (RBF) KernelSigmoid Kernel

What kernel can be used in SVM to handle non-linear data?Answer areaLinear KernelPolynomial KernelRadial Basis Function (RBF) KernelAll of the above