Produces probabilites for each outcome class and writes the probability of the max encoded class into a SQL table.

Argument and Default Value

Feature name for the SQL table.


Given a classification model (--load_model), this switch will predict probabilities on the groups given in the outcome table, for each class, and puts the probability of the max encoded class into a MySQL 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.

The table created will look like:


where modelType is the first 4 letter of the model name. If you used rfc for instance, it will look like


Make sure the features are in the right order (i.e. the order they were put into when creating the model). A good place to check for that is the name of the pickle file (if you're using a pre:doc:fwflag_made picklefile, like those in here)

You need to make an output table that contains non null values for the outcomes & groups that you want probabilities for, cause it uses the --predict_classifiers code to run this, which is why it also outputs comparisons between the values in the outcome table and the predicted outcomes.

See Applying A Pickle Model for more details on applying pickled models.

Other Switches

Required Switches:

Example Commands

dlatkInterface.py -d dla_tutorial -t msgs -c user_id -f 'feat$cat_met_a30_2000_cp_w$msgs$user_id$1gra'  \
--outcome_table blog_outcomes --outcomes genderDummy \
--predict_probabilities_to_feats lbp_prob_gender  --load --picklefile \