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Train, Validation and Test

Fit on one pile, choose on another, report on a third that was never consulted. Then k-fold cross-validation, and six pipelines on pure-noise labels — three of which score far above chance.

Builds onOverfitting and Underfitting

Every fit and every cross-validation run computed in your browser · seeded data

Act 1 of 6 · Three pilesstep 1 of 27

40 points

train[n, x,y] =[24, 2]val[n, x,y] =[8, 2]test[n, x,y] =[8, 2]

Training fits the coefficients. Validation chooses between models. Test reports, once.

Speed

Forty noisy measurements of one curve. We want a polynomial that predicts new measurements well, and an honest number for how well — two different jobs.

40 = \underbrace{24}_{\text{train}} + \underbrace{8}_{\text{validation}} + \underbrace{8}_{\text{test}}

Three piles, three jobs

Training — fits the parameters (the coefficients).
Validation — chooses between models and settings (the degree). Its score becomes optimistic as soon as you choose by it.
Test — opened once, to report. Never used to decide anything.

A common misconception

“Cross-validation gives an honest score, whatever you did to the data before it.”

On 40 rows of pure noise, picking 10 of 200 features with every label and then cross-validating averaged 83% accuracy over 100 runs — on data where nothing can be predicted. Doing the same selection inside each fold averaged 50%.

Check yourself

Question 1 of 3

You compare 30 models and keep the one with the best validation score. Why is that validation score now an optimistic estimate of its error?

Train, Validation and Test

Fit on one pile, choose on another, report on a third that was never consulted. Then k-fold cross-validation, and six pipelines on pure-noise labels — three of which score far above chance.

Builds onOverfitting and Underfitting

Every fit and every cross-validation run computed in your browser · seeded data

Act 1 of 6 · Three pilesstep 1 of 27

40 points

train[n, x,y] =[24, 2]val[n, x,y] =[8, 2]test[n, x,y] =[8, 2]

Training fits the coefficients. Validation chooses between models. Test reports, once.

Speed

Forty noisy measurements of one curve. We want a polynomial that predicts new measurements well, and an honest number for how well — two different jobs.

40 = \underbrace{24}_{\text{train}} + \underbrace{8}_{\text{validation}} + \underbrace{8}_{\text{test}}

Three piles, three jobs

Training — fits the parameters (the coefficients).
Validation — chooses between models and settings (the degree). Its score becomes optimistic as soon as you choose by it.
Test — opened once, to report. Never used to decide anything.

A common misconception

“Cross-validation gives an honest score, whatever you did to the data before it.”

Check yourself

Question 1 of 3

You compare 30 models and keep the one with the best validation score. Why is that validation score now an optimistic estimate of its error?

DSAverse

Train, Validation and Test

See also

Train, Validation and Test

See also