Algorithms are the core of machine learning solutions. An algorithm is a step-by-step process or set of rules for solving a problem. There are scores of different algorithms and researchers are constantly producing new ones.

It is impossible to know in advance which machine learning algorithm will perform the best for a given problem; the only way is to try as many algorithms as possible – this is known as No Free Lunch Theorem.

Some of the commonly used supervised learning algorithms are listed below. .

  • Naïve Bayes – classification tasks only
  • Logistic regression – classification tasks only
  • Linear regression – regression tasks only
  • Artificial neural network (ANN)
  • Decision tree
  • K-nearest neighbors (k-NN)
  • Support vector machines (SVM)
  • Ensemble methods – stacking, bagging (eg, random forest), boosting (eg, AdaBoost, Gradient Boosted Trees), voting
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