How does SVM handle outliers in the training data?
Practice Questions
Q1
How does SVM handle outliers in the training data?
By ignoring them completely
By assigning them a higher weight
By using a soft margin approach
By clustering them separately
Questions & Step-by-Step Solutions
How does SVM handle outliers in the training data?
Step 1: Understand that SVM stands for Support Vector Machine, which is a type of machine learning model used for classification tasks.
Step 2: Recognize that outliers are data points that are significantly different from other points in the training data.
Step 3: Learn about the 'hard margin' approach, where SVM tries to perfectly separate classes without allowing any misclassifications, which can be problematic with outliers.
Step 4: Understand the 'soft margin' approach, where SVM allows some misclassifications (or errors) to create a more flexible boundary between classes.
Step 5: Realize that by allowing some misclassifications, SVM can ignore or reduce the influence of outliers, leading to a better overall model performance.
Soft Margin – A technique used in SVM that allows for some misclassifications to improve the model's generalization by balancing the trade-off between maximizing the margin and minimizing classification errors.
