How does the Kernel Trick help in dealing with non-linear data in SVM?
- Enhances data visualization
- Maps data into higher-dimensional space for linear separation
- Reduces data size
- Speeds up computation
The Kernel Trick helps in dealing with non-linear data by mapping it into a higher-dimensional space where it can be linearly separated.
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