nClassification:
misleading error measure
nStatistical
regression: learn a continuous function of features that predicts log of running time
nSupervised
learning: data broken into 80% training
set, 20% test set
nStarted
with simplest technique: linear regression
nfind a hyperplane that minimizes root mean squared error (RMSE) on training data
nLinear
regression is useful:
nas
a (surprisingly good) baseline
nyields a very interpretable model with understandable variables