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Fast Iterative Shrinkage/Thresholding Classifier

An estimator that employs the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) for training linear classifiers. Designed for fast convergence, FISTA is particularly well-suited for large-scale and high-dimensional problems. It encourages sparse solutions and is efficient in terms of memory usage. The algorithm is ideal for convex optimization problems and offers flexibility in regularization techniques.

Advantages:​

  • Fast convergence, especially suitable for large datasets.
  • Encourages sparse solutions, advantageous in high-dimensional settings.
  • Resource-efficient, doesn't require storing large intermediate matrices.

Disadvantages:​

  • Sensitive to the choice of hyperparameters, requiring careful tuning.
  • May suffer from numerical instabilities.