evaluating machine-learning models this is the simplest evaluation protocoland it suffers from one flawif little data is availablethen your validation and test sets may contain too few samples to be statistically representative of the data at hand this is easy to recognizeif different random shuffling rounds of the data before splitting end up yielding very different measures of model performancethen you're having this issue -fold validation and iterated -fold validation are two ways to address thisas discussed next -fold validation with this approachyou split your data into partitions of equal size for each partition itrain model on the remaining partitionsand evaluate it on partition your final score is then the averages of the scores obtained this method is helpful when the performance of your model shows significant variance based on your traintest split like hold-out validationthis method doesn' exempt you from using distinct validation set for model calibration schematicallyk-fold cross-validation looks like figure listing shows simple implementation data split into partitions fold validation training training validation score # fold validation validation training validation score # fold validation training validation validation score # figure final scoreaverage three-fold validat ion listing -fold cross-validat ion num_validation_samples len(data/ np random shuffle(dataselects the validationvalidation_scores [data partition for fold in range( )validation_data data[num_validation_samples foldnum_validation_samples (fold )training_data data[:num_validation_samples folddata[num_validation_samples (fold ):model get_model(model train(training_datavalidation_score model evaluate(validation_datavalidation_scores append(validation_scoreuses the remainder of the data as training data note that the operator is list concatenationnot summation creates brand-new instance of the model (untrainedlicensed to