only tell me correct answer no need for working the group of blue balls and red balls are separable by a straight line/linear lineLet's consider a scenario like shown belowX2O SVM can not classify this data.X1X2Here we have one blue ball in the boundary of the red ball. So how does SVM classify the data?O SVM is not robust to outliers.X1O SVM give us error.O The blue ball in the boundary of red ones is an outlier of blue balls. The SVM algorithm has the characteristics to ignore the outlier and findsthe best hyperplane that maximizes the margin. SVM is robust to outliers.

Support Vector Machines (SVM) is a robust classification algorithm that can handle outliers by using…

the group of blue balls and red balls are separable by a straight line/linear line Let's consider a scenario like shown below X2 XI X2 X1 Here we have one blue ball in the boundary of the red ball. So how does SVM classify the data? OSVM can not classify this data. OSVM give us error. The blue ball in the boundary of red ones is an outlier of blue balls. The SVM algorithm has the characteristics to ignore the outlier and finds the best hyperplane that maximizes the margin. SVM is robust to outliers. OSVM is not robust to outliers.

the group of blue balls and red balls are separable by a straight line/linear line Let's consider a scenario like shown below X2 XI X2 X1 Here we have one blue ball in the boundary of the red ball. So how does SVM classify the data? OSVM can not classify this data. OSVM give us error. The blue ball in the boundary of red ones is an outlier of blue balls. The SVM algorithm has the characteristics to ignore the outlier and finds the best hyperplane that maximizes the margin. SVM is robust to outliers. OSVM is not robust to outliers.