3 1 Cross Validation | Machine Learning

*CROSS-VALIDATION*
An easier way to evaluate the model is to use cross-validation.
The procedure for K-fold cross-validation is very simple:
1. Randomly split the data into K roughly equal groups.
2. Learn the model on K − 1 groups and predict the held-out Kth group.
3. Do this K times, holding out each group once.
4. Evaluate performance using the cumulative set of predictions.
For the case of the regularization parameter λ, the above sequence can be
run for several values with the best-performing value of λ chosen.
The data you test the model on should never be used to train the model!
#crossvalidation #test #testing #evaluation #evaluate #validated #validating

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