Review:
Support Vector Machine (svm)
overall review score: 4.5
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score is between 0 and 5
A Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It works by finding the optimal hyperplane that separates different classes in a high-dimensional feature space.
Key Features
- Effective in high-dimensional spaces
- Versatile in handling non-linear data through kernel tricks
- Memory efficient due to using only a subset of training points as support vectors
Pros
- High accuracy in classification tasks
- Effective in cases where the number of dimensions is greater than the number of samples
- Can handle non-linear decision boundaries through kernel trick
Cons
- Sensitive to outliers in the data
- Can be computationally expensive for large datasets
- Selection of a suitable kernel function can be challenging