Regression
supervised learning: take examples of inputs and outputs. Now, given a new input, predict its output.
Mapping continuous inputs to outputs.
discrete, continuous
child Height, parent height
2/3 < 1, regression to mean
Reinforcement learning
Regression in machine learning
Finding the best constant function
f(x) = c
E(c) = Σi=1(yi-c)^2
LOSS, ERROR
Order of polynomial
k = 0:constant
k = 1:line
k = 2:parabola
f(x) = c0 + cix + c2x^2 + ... ckX^k
polynomial regression
c0 + c1x + c2x^2 + c3x^3 = y
Errors
Training data has errors not modeling f, but f + ε where do errors come from?
sensor error
Cross Validation
Fundamental assumption
use a model that is complex enough to fit the data without causing problems on the test set
-training error
-cross validation error
-> scalar input, continuous
-> vector input, continuous
include more input features (size, distance from zoo)
predict credit score
job? age? assets?
-> distance, vector or scalar