--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 (-g) and a column for each outcome in the model.

Other Switches

Required Switches: -d, -g, -t, -f --load_model and --picklefile Example Commands ================

Using the packaged dla_tutorial dataset (see Applying A Pickle Model): loads the regression model in age.pickle, uses the 1grams in feat$1gram$msgs$user_id$16to16$0_01 to predict age, and inserts those values into a table called p_ridg$lbp_age.

dlatkInterface.py -d dla_tutorial -t msgs -c user_id -f 'feat$1gram$msgs$user_id$16to16$0_01' \
--load_model --picklefile age.pickle --predict_regression_to_outcome_table lbp_age