Different performance by different ML classifiers, what can I deduce?

Question

I have used a ML approach to my research using python scikit-learn. I found that SVM and logistic regression classifiers work best (eg: 85% accuracy), decision trees works markedly worse (65%), and then Naive Bayes works markedly worse (40%).

I will write up the conclusion to illustrate the obvious that some ML classifiers worked better than the others by a large margin, but what else can I say about my learning task or data structure based on these observations?

Edition:

The data set involved 500,000 rows, and I have 15 features but some of the features are various combination of substrings of certain text, so it naturally expands to tens of thousands of columns as a sparse matrix. I am using people's name to predict some binary class (eg: Gender), though I feature engineer a lot from the name entity like the length of the name, the substrings of the name, etc.

Answer 1

I recommend you to visit this awesome map on choosing the right estimator by the scikit-learn team http://scikit-learn.org/stable/tutorial/machine_learning_map

As describing the specifics of your own case would be an enormous task (I totally understand you didn't do it!) I encourage you to ask yourself several questions. Thus, I think the map on 'choosing the right estimator' is a good start.

Literally, go to the 'start' node in the map and follow the path:

• is my number of samples > 50?

And so on. In the end you might end at some point and see if your results match with the recommendations in the map (i.e. did I end up in a SVM, which gives me better results?). If so, go deeper into the documentation and ask yourself why is that one classifier performing better on text data or whatever insight you get.

As I told you, we don't know the specifics of your data, but you should be able to ask such questions: what type of data do I have (text, binary, ...), how many samples, how many classes to predict, ... So ideally your data is going to give you some hints about the context of your problem, therefore why some estimators perform better than others.

But yeah, your question is really broad to grasp in a single answer (and specially without knowing the type of problem you are dealing with). You could also check if there might by any of those approaches more inclined to overfit, for example.

The list of recommendations could be endless, this is why I encourage you to start defining the type of problem you are dealing with and your data (plus to the number of samples, is it normalized? Is it disperse? Are you representing text in sparse matrix, are your inputs floats from 0.11 to 0.99).

Anyway, if you want to share some specifics on your data we might be able to answer more precisely. Hope this helped a little bit, though ;)