DAT7 - 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?	Outputs 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	Somewhat	No	Yes	KNN
3	Linear regression	Regression	Yes	Yes	Lower	Fast	Fast	None (excluding regularization)	Yes	No	No	N/A	Yes	No (unless regularized)	Linear regression
4	Logistic regression	Classification	Somewhat	Somewhat	Lower	Fast	Fast	None (excluding regularization)	Yes	No	No	Yes	Yes	No (unless regularized)	Logistic regression
5	Naive Bayes	Classification	A little	Somewhat	Lower	Fast	Fast	None	Yes	Yes	No	No	Yes	No	Naive Bayes
6	Decision trees	Either	Somewhat	Somewhat	Lower	Fast	Fast	Some	No	Somewhat	Yes	Somewhat	No	No	Decision trees
7	Random Forests	Either	A little	No	Higher	Slow	Moderate	Some	No	Yes	Yes	Somewhat	No	No	Random Forests
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9	Answer options:	Classification	Yes	Yes	Higher	Fast	Fast	Some	Yes	Yes	Yes	Yes	Yes	Yes
10		Regression	Somewhat	Somewhat	Lower	Moderate	Moderate	Minimal	Somewhat	Somewhat	No	Somewhat	No	No
11		Either	A little	A little		Slow	Slow	None	No	No		No
12			No	No								N/A
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