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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