Review:
Support Vector Machines
overall review score: 4.5
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score is between 0 and 5
Support Vector Machines (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It finds the hyperplane that best separates different classes in a high-dimensional space.
Key Features
- Effective in high-dimensional spaces
- Works well with small to medium-sized datasets
- Ability to handle non-linear data through kernel tricks
Pros
- High accuracy in classification tasks
- Versatile in handling various types of data
- Robust against overfitting
Cons
- Can be computationally expensive with large datasets
- Sensitive to kernel selection and hyperparameters tuning