mod 08: overfitting, regularization

overfitting occurs when a model fits the — data very wells but performs poorly on — data

training, test

underfitting occurs when a model is too — and cannot capture the — in the data

simple, pattern

a model that performs well on new data is said to — well

generalize

a linear model like f(x) = w0 + w1x can lead to — if the true relationship is more complex

underfitting

a high degree polynomial model may lead to — because it fits noise in the data

overfitting

an overfit regression model typically has very — training error and — test error

low, high

underfitting is associated with high —

bias

overfitting is associated with high —

variance

variance refers to how sensitive a model is to — — in the — data

small changes, training

bias refers to error caused by overly — assumptions

simple

increasing model complexity TOO MUCH leads to —

overfitting

decreasing model complexity TOO MUCH leads to —

underfitting

FIXING OVERFITTING

• collect more —

• selecting only important input variables: — —

data, feature selection

feature selection helps reduce — by — model complexity

overfitting, lowering