Machine Learning Cheat Sheet Map
scikit-learn.org
Machine Learning Cheat Sheet Map
1–10 of 18 posts
Re: Machine Learning Cheat Sheet Map
#2Just a note. scikit has logistic regression, but it isn't shown at the top level. It is contained within `LinearSVC` on the diagram: http://scikit-learn.org/stable/modules/generated/sklearn.lin...
Re: Machine Learning Cheat Sheet Map
#3I don't see why the diagram has SVC and ensemble classifiers located in the "not working" path from KNeighbors Classifiers. It is reasonable to use an ensemble method independent of whether nearest neighbors works.
Re: Machine Learning Cheat Sheet Map
#4The "text data" decision point seems arbitrary and in my opinion, not useful. I'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.
Re: Machine Learning Cheat Sheet Map
#5I'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 / detail tradeoff.
Re: Machine Learning Cheat Sheet Map
#6The "text data" decision point seems arbitrary and in my opinion, not useful. I'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.
I guess the chart is designed to those who are doing their first steps in the machine learning world. It makes order.
BTW I have nothing to do with scikit-learn, I shared it as I found it to be useful.
Re: Machine Learning Cheat Sheet Map
#7Dimensionality 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. :)
Re: Machine Learning Cheat Sheet Map
#8The "text data" decision point seems arbitrary and in my opinion, not useful. I'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.
I guess the chart is designed to those who are doing their first steps in the machine learning world. It makes order. BTW I have nothing to do with scikit-learn, I shared it as I found it to be useful.
Thanks for sharing it. I think it is a good start; it gives people something to react and build upon.
Re: Machine Learning Cheat Sheet Map
#9The "text data" decision point seems arbitrary and in my opinion, not useful. I'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.
Seeing this (a while ago) from the perspective of someone that was a complete noob to machine learning I found it extremely useful. It my not be the end-all-be-all, but I found it a useful graphic for figuring out which algorithms I should research to solve a specific ML problem.
Re: Machine Learning Cheat Sheet Map
#10"This project has been temporarily blocked for exceeding its bandwidth threshold" I wonder why no one uses SourceForge anymore..