Supervised Learning - Classification Week 7 Challenge

1.

Support Vector Machines (SVMs) are a type of supervised learning algorithm that can be used for classification and regression tasks.

Advantages of SVM:

1. It performs well on high-dimensional and small-sized datasets.
2. It can handle non-linear decision boundaries.
3. It is effective in cases where the number of features is greater than the number of samples.
4. It is resistant to overfitting and works well on noisy data.

Disadvantages of SVM:

1. It is computationally expensive and may not scale well to large datasets.
2. It is sensitive to the choice of kernel and hyperparameters.
3. It is difficult to interpret the model because it is a black box model.
4. It may not perform well on imbalanced datasets.