Why use Support Vector Machine?

Q1: Is Support Vector Machine a simple or difficult classifier to understand?

Answer: Simple

Q2: Can Support Vector Machine solve linear problems or non-linear problems?

Answer: Linear Problems & Non-Linear Problems

Q3: Does Support Vector Machine increase in error as the number of features increases?

Answer: No Curse of Dimensionality

Q4: Can Support Vector Machine handle outliers or is sensitive to outliers?

Answer: Handle Outliers

Q5: Is Support Vector Machine a fast or slow classifier?

Answer: Slow

Q6: Can Support Vector Machine handle missing data or sensitive to missing data?

Answer: Sensitive to Missing Data

Q7: Can Support Vector Machine handle imbalanced data or sensitive to imbalanced data?

Answer: Sensitive to Imbalanced Data

Q8: Can Support Vector Machine handle multicollinearity among independent variables or is sensitive to multicollinearity?

Answer: Sensitive to Multicollinearity in Independent Variables

Q9: Is Support Vector Machine more prone to overfitting or less prone to overfitting?

Answer: More prone to overfitting with hard margin SVM; less prone to overfitting with soft margin SVM

Q10: Is Support Vector Machine an interpretable classifier or not an interpretable classifier?

Answer: Interpretable for Linear SVM but not for Kernel SVM (RBF, Polynomial, Sigmoid)