DAT4 - Comparing Supervised Learning Algorithms

	A	B	C	D	E	F	G	H	I	J	K	L	M	N	O
1	Algorithm	Problem Type	Results interpretable by you?	Easy to explain algorithm to others?	Average predictive accuracy	Training speed	Prediction speed	Amount of parameter tuning needed (excluding feature selection)	Performs well with small number of observations?	Handles lots of irrelevant features well (separates signal from noise)?	Automatically learns feature interactions?	Gives calibrated probabilities of class membership?	Parametric?	Features might need scaling?	Algorithm
2	KNN	Either	Yes	Yes	Lower	Fast	Depends on n	Minimal	No	No	No	Yes	No	Yes	KNN
3	Linear regression (unregularized)	Regression	Yes	Yes	Lower	Fast	Fast	None	Yes	No	No	N/A	Yes	No	Linear regression (unregularized)
4	Logistic regression (unregularized)	Classification	Somewhat	Somewhat	Lower	Fast	Fast	None	Yes	No	No	Yes	Yes	No	Logistic regression (unregularized)
5	Naive Bayes	Classification	Somewhat	Somewhat	Lower	Fast (excluding feature extraction)	Fast	Some for feature extraction	Yes	Yes	No	No	Yes	No	Naive Bayes
6	Decision trees	Either	Somewhat	Somewhat	Lower	Fast	Fast	Some	No	No	Yes	Possibly	No	No	Decision trees
7	Random Forests	Either	A little	No	Higher	Slow	Moderate	Some	No	Yes (unless noise ratio is very high)	Yes	Possibly	No	No	Random Forests
8	Regularized linear regression	Regression	Somewhat	No	Higher	Fast	Fast	Some	Yes	Yes	No	N/A	Yes	Yes	Regularized linear regression
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