In my udacity machine learning class I was confronted with this list of Supervised Learning Models (lots of information here) from scikit-learn:
- Gaussian Naive Bayes (GaussianNB)
- Decision Trees
- Ensemble Methods (Bagging, AdaBoost, Random Forest, Gradient Boosting)
- K-Nearest Neighbors (KNeighbors)
- Stochastic Gradient Descent Classifier (SGDC)
- Support Vector Machines (SVM)
- Logistic Regression
and was asked to choose the best three for a particular problem.
This helped me in narrowing down which one to use:
http://scikit-learn.org/stable/tutorial/machine_learning_map/