scikit learn output metrics.classification_report into CSV/tab-delimited format

Question

I\'m doing a multiclass text classification in Scikit-Learn. The dataset is being trained using the Multinomial Naive Bayes classifier having hundreds of labels. Here\'s an extr

Answer 1

We can get the actual values from the precision_recall_fscore_support function and then put them into data frames. the below code will give the same result, but now in pandas df :).

clf_rep = metrics.precision_recall_fscore_support(true, pred)
out_dict = {
             "precision" :clf_rep[0].round(2)
            ,"recall" : clf_rep[1].round(2)
            ,"f1-score" : clf_rep[2].round(2)
            ,"support" : clf_rep[3]
            }
out_df = pd.DataFrame(out_dict, index = nb.classes_)
avg_tot = (out_df.apply(lambda x: round(x.mean(), 2) if x.name!="support" else  round(x.sum(), 2)).to_frame().T)
avg_tot.index = ["avg/total"]
out_df = out_df.append(avg_tot)
print out_df