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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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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