MultiFC: Evaluation

The problem is a multiclass classification problem. Each sample (a Claim) contains the context in which they occured, evidence pages and rich metadata. You must predict the Claim veracity. Labels include both straight-forwardratings of veracity (‘correct’, ‘incorrect’), but alsolabels that would be more difficult to map onto alinear veracity scale (e.g. ‘grass roots movement!’,‘misattributed’, ‘not the whole story’).  
You are given for training a data matrix X_train of dimension num_training_samples x num_features and an array y_train of labels of dimension num_training_samples. You must train a model which predicts the labels for the matrix X_test. 
To prepare your submission, remember to use predict_proba, which provides a matrix of prediction scores scaled between 0 and 1. The dimension of the matrix is num_pattern x num_classes. Each line represents the probabilities of class membership, which sum up to one.  

This is the process:

This sample competition allows you to submit either:

The submissions are evaluated using the f1_score metric with the two options 'micro' and 'macro'.