Disables column wise z-scoring of outcomes/features for regression/classification.

Argument and Default Value

False (i.e. standardizing is the default)


Usually, every outcome is z:doc:fwflag_scored, and so are the group_norms for every feature, but this switch disables that. This can sometimes improve prediction performance though usually it's slightly worse than with standardizing.

--regression_to_lexicon, --classification_to_lexicon need this flag.

Other Switches

Required Switches: -d, -g, -t, -f, --outcome_table, --outcomes Won't do anything without any of these switches: --train_regression, --nfold_test_regression, etc. --train_classifiers, --nfold_test_classifiers, etc. --regression_to_lexicon --classification_to_lexicon Example Commands ================ .. code:doc:fwflag_block:: python

# Trains a regression model to predict age for users from 1grams, without standardizing # Will save the model to a picklefile called deleteMe.pickle, and create a lexicon called testAgeLex ~/fwInterface.py -d fb20 -t messages_en -g user_id -f 'feat$1gram$messages_en$user_id$16to16$0_01' --outcome_table masterstats_andy_r10k --outcomes age --train_regression --save_model --picklefile deleteMe.pickle --no_standardize --regression_to_lexicon testAgeLex