+ +Training proceeds by splitting the data into a training and test set, and training is stopped when test set performance +(on the reduced form prediction error) starts to degrade. + +The output is an estimated function :math:`\hat{g}`. To obtain an estimate of :math:`\tau`, we ...
For more on the train-test split, see the tutorial: Train-Test Split for Evaluating Machine Learning Algorithms The k-fold cross-validation procedure involves dividing a dataset into k non-overlapping partitions and using one fold as the test set and all other folds as the training set. A mo...
and then applied to the validation/test data. E.g. computing the mean and subtracting it from every image across the entire dataset and then splitting the data into train/val/test splits would be a mistake.
experiment. Alternatively, you may quickly learn that your dataset does not expose enough structure for any mainstream algorithm to do well. Spot-checking gives you the results you need to decide whether to move forward and optimize a given model or backward and revisit the presentation o...
“… any preprocessing statistics (e.g. the data mean) must only be computed on the training data, and then applied to the validation/test data. E.g. computing the mean and subtracting it from every image across the entire dataset and then splitting the data into train/val/test splits ...
For example, if you do a PCA on your original, untouched data, use PC1 and PC2 as "new" features, and then split your dataset into train and test, you are leaking information from training set into test set. That will boost your score up. You mentioned that af...