Support vector machine:

Support vector machines (SVMs) are a type of supervised machine learning algorithm that can be used for both classification and regression tasks. However, SVMs are most commonly used for classification.

SVMs work by finding a hyperplane in the feature space that separates the data points into two classes with the maximum margin possible. The margin is the distance between the hyperplane and the closest data points of each class.

Finding the maximum-margin hyperplane is a quadratic programming problem, which can be solved efficiently using a variety of algorithms. Once the maximum-margin hyperplane is found, it can be used to classify new data points by predicting which side of the hyperplane they fall on.

Advantages:

  • SVMs are very effective in high-dimensional spaces.
  • SVMs are able to learn from relatively small datasets.
  • SVMs are robust to noise and outliers.
  • SVMs are able to learn complex relationships between the features.

Disadvantages:

  • Large datasets: SVM is not suitable for large datasets.
  • Training time: SVM has a long training time for large datasets.
  • Computation: SVM has costly computation.
  • Memory: SVM has extensive memory requirements.
  • Not well-suited for noisy datasets.