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Machine Learning Cheat Sheet Map

114 点作者 cognibits超过 10 年前

10 条评论

dj-wonk超过 10 年前
Just a note. scikit has logistic regression, but it isn&#x27;t shown at the top level. It is contained within `LinearSVC` on the diagram: <a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression" rel="nofollow">http:&#x2F;&#x2F;scikit-learn.org&#x2F;stable&#x2F;modules&#x2F;generated&#x2F;sklearn.lin...</a>
ColinWright超过 10 年前
It&#x27;s currently down - is it the same one discussed at some length a year ago?<p><a href="https://news.ycombinator.com/item?id=5831512" rel="nofollow">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=5831512</a><p>Here are some other resources for machine learning, not necessarily restricted to the algorithms implemented in SciKit:<p><a href="http://eferm.com/wp-content/uploads/2011/05/cheat3.pdf" rel="nofollow">http:&#x2F;&#x2F;eferm.com&#x2F;wp-content&#x2F;uploads&#x2F;2011&#x2F;05&#x2F;cheat3.pdf</a><p><a href="http://peekaboo-vision.blogspot.ca/2013/01/machine-learning-cheat-sheet-for-scikit.html" rel="nofollow">http:&#x2F;&#x2F;peekaboo-vision.blogspot.ca&#x2F;2013&#x2F;01&#x2F;machine-learning-...</a><p><a href="http://rise.cse.iitm.ac.in/wiki/index.php/Introduction_to_Machine_Learning" rel="nofollow">http:&#x2F;&#x2F;rise.cse.iitm.ac.in&#x2F;wiki&#x2F;index.php&#x2F;Introduction_to_Ma...</a><p><a href="http://mlg.eng.cam.ac.uk/creed/Notes/ML_Compendium.pdf" rel="nofollow">http:&#x2F;&#x2F;mlg.eng.cam.ac.uk&#x2F;creed&#x2F;Notes&#x2F;ML_Compendium.pdf</a>
Pamar超过 10 年前
&quot;Mathbabe&quot; is currently working on something similar: <a href="http://mathbabe.org/2014/08/28/a-decision-tree-for-decision-trees/" rel="nofollow">http:&#x2F;&#x2F;mathbabe.org&#x2F;2014&#x2F;08&#x2F;28&#x2F;a-decision-tree-for-decision-...</a> even if she has just started working on this (and she mentions the OP item herself).
dj-wonk超过 10 年前
Dimensionality reduction can be a goal in and of itself, but many of the same techniques (i.e. feature selection) are useful precursors for classification, clustering, and regression. It would be nice to capture that on the diagram. More arrows, please. :)
dj-wonk超过 10 年前
The &quot;text data&quot; decision point seems arbitrary and in my opinion, not useful. I&#x27;ve analyzed text data with a Naive Bayes a classifier as well as SVM. I really like what the chart is trying to be, but I think it is editorializing too much.
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dj-wonk超过 10 年前
I don&#x27;t see why the diagram has SVC and ensemble classifiers located in the &quot;not working&quot; path from KNeighbors Classifiers. It is reasonable to use an ensemble method independent of whether nearest neighbors works.
lazycrazyowl超过 10 年前
This looks very similar to the map described on dlib C++ Machine Learning library page. <a href="http://dlib.net/ml_guide.svg" rel="nofollow">http:&#x2F;&#x2F;dlib.net&#x2F;ml_guide.svg</a>
dj-wonk超过 10 年前
I&#x27;d suggest giving Random Forests a call-out instead of leaving them hidden under ensemble methods in the diagram. I realize this is a clarity &#x2F; detail tradeoff.
iLoch超过 10 年前
&quot;This project has been temporarily blocked for exceeding its bandwidth threshold&quot; I wonder why no one uses SourceForge anymore..
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a8da6b0c91d超过 10 年前
dlib did something similar with different results.<p><a href="http://dlib.net/ml_guide.svg" rel="nofollow">http:&#x2F;&#x2F;dlib.net&#x2F;ml_guide.svg</a>