Predicts the outcomes and compares the predicted values to the actual ones.
Argument and Default Value¶
Given a model (--load_model), this switch will predict the outcome classes on the groups given in the outcome table and compare them to the actual classes in the outcome table. Make sure the feature tables are in the same order as they were when the model was created.
The output will contains some of the following things: confusion matrix See here. precision and recall See here and this line (numbers are examples):
FOLD ACC: 0.8839 (mfclass_acc: 0.6186); mfclass: 1; auc: 0.8749
ACC percent classified correctly mfclass_acc accuracy if we predicted the main class every time (baseline) mfclass "Most frequent class" i.e. the class that has the most groups in it. auc Area Under the Curve (the ROC curve). See here for more explanations.
Required Switches: -d, -c, -t, -f, --outcome_table, --outcomes --load_model and --picklefile Optional Switches: --classification_to_lexicon --group_freq_thresh Example Commands ================ .. code:doc:fwflag_block:: python
# Loads the classification model in deleteMeGender.pickle, and uses the features to predict the gender # of users in masterstats_andy_r10k, and compares the predicted genders to the actual ones in the table. ~/fwInterface.py -d fb20 -t messages_en -c user_id -f 'feat$1gram$messages_en$user_id$16to16$0_01' --outcome_table masterstats_andy_r10k --outcomes gender --load_model --picklefile deleteMeGender.pickle --predict_classifiers