# --predict_regression_to_outcome_table¶

## Switch¶

--predict_regression_to_outcome_table

## Description¶

Predicts the outcomes and puts the predicted values into a SQL outcome table

## Argument and Default Value¶

Name of the new outcome table

## Details¶

Given a model (--load_model), this switch will predict the outcomes on the groups given in the feature table and puts the values into a MySQL outcome table. This is useful for a set of groups that you don't have the outcomes for, but you have a prediction model for it (here are the outcomes we can predict from language). The table created will look like: p_modelType$ARGUMENT If you used ridge for instance, it will look like p_ridg$ARGUMENT.

Make sure the features are in the right order (i.e. the order they were put into when creating the model).

The table will have one column for the correlation field (-c) and a column for each outcome in the model.

## Other Switches¶

Required Switches: -d, -c, -t, -f --load_model and --picklefile Example Commands ================ .. code:doc:fwflag_block:: python

# Loads the regression model in deleteMe.pickle, and uses the features to predict the ages of the users in # feat$1gram$messages_en$user_id$16to16$0_01, and inserts those values into a table called p_ridg$deleteMe. ~/fwInterface.py -d fb20 -t messages_en -c user_id -f 'feat$1gram$messages_en$user_id$16to16$0_01' --load_model --picklefile deleteMe.pickle --predict_regression_to_outcome_table deleteMe