This resource is designed primarily for beginning data scientists or
analysts who are interested in identifying and applying machine learning
algorithms to address the problems of their interest.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
The machine learning algorithm cheat sheet helps you
to choose from a variety of machine learning algorithms to find the
appropriate algorithm for your specific problems. This article walks you
through the process of how to use the sheet.
Since the cheat sheet is designed for beginner data scientists and
analysts, we will make some simplified assumptions when talking about
the algorithms.
The algorithms recommended here result from compiled feedback and
tips from several data scientists and machine learning experts and
developers. There are several issues on which we have not reached an
agreement and for these issues we try to highlight the commonality and
reconcile the difference.
Additional algorithms will be added in later as our library grows to encompass a more complete set of available methods.
How to use the cheat sheet
Read the path and algorithm labels on the chart as "If then use ." For example:
If you want to perform dimension reduction then use principal component analysis.
If you need a numeric prediction quickly, use decision trees or logistic regression.
If you need a hierarchical result, use hierarchical clustering.
Sometimes more than one branch will apply, and other times none of
them will be a perfect match. It’s important to remember these paths are
intended to be rule-of-thumb recommendations, so some of the
recommendations are not exact. Several data scientists I talked with
said that the only sure way to find the very best algorithm is to try
all of them.
链接：
http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
原文链接：
http://weibo.com/1402400261/EEjnBpy6u?from=page_1005051402400261_profile&wvr=6&mod=weibotime&type=comment#_rnd1492077060567
