Originally published by in 2013, it still is a goldmine for all machine learning professionals. The algorithms are broken down in several categories. Here we provide a high-level summary, a much longer and detailed version can be found here. You can even download an algorithm map from the original article. Below is a much smaller version.
It would be interesting to list, for each algorithm,
and generally speaking, compare these algorithms. I would add HDT, Jackknife regression, density estimation, attribution modeling (to optimize marketing mix), linkage (in fraud detection), indexation (to create taxonomies or for clustering large data sets consisting of text), bucketisation, and time series algorithms.
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Ensemble methods to fit data: see original paper