3 ms·
I agree good models like xgboost get buried and inexperienced practitioners jump to deep models too quickly. Often without understanding how to properly archite
by soraki_soladead 8y ago
I agree good models like xgboost get buried and inexperienced practitioners jump to deep models too quickly. Often without understanding how to properly architect and tune them. Always start with a simple baseline (EDIT: and good process).
However, what's lacking in the ML practitioner community is nuance. Some applications need deep models some problems need xgboost. There isn't a "best" model in text classification because it depends on your data and problem.
- asah 8y agoThis chart is great for newbies, perhaps give it an update? https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html https://scikit-learn.org/stable/tutorial/machine_learning_ma...
- autokad 8y agoI'm not a huge fan of the chart as it looks pretty 2014 dated. SVMs are rarely used anymore, so perhaps if you replaced all of them (including in the regression part) with gbm. Didn't notice any mention of logistic regression, which I still heavily use as well. dimentionality reduction is part of any step, which depends on number of observations, features, and model. my favorite is l1 or pca, but I am not afraid to use stepwise regression or some tree method.