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Which machine learning algorithm should I use?

By Hui Li, Principal Staff Scientist, Data Science, at SAS.

A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm should I use?” The answer to the question varies depending on many factors, including:

  • The size, quality, and nature of data.
  • The available computational time.
  • The urgency of the task.
  • What you want to do with the data.

Even an experienced data scientist cannot tell which algorithm will perform the best before trying different algorithms. We are not advocating a one and done approach, but we do hope to provide some guidance on which algorithms to try first depending on some clear factors.

The machine learning algorithm cheat sheet

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Flow chart shows which algorithms to use when

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The article describes when using one of the following algorithms:

  • Linear regression and Logistic regression 
  • Linear SVM and kernel SVM
  • Trees and ensemble trees
  • Neural networks and deep learning
  • k-means/k-modes, GMM (Gaussian mixture model) clustering
  • Hierarchical clustering
  • PCA, SVD and LDA

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