I computed a word vector model on medical reports on a critical disease and run a logistic regression on a binary classifier. Text data is labeled with 1=successful and 0=non-successful for the true outcome of the treatment. I train with 90% of my data and test on 10%. The dataset contains about 30% successful and 70% unsuccessful cases (N= ca. 20,000).
Now, I got following results from my
LogR = LogisticRegression(penalty='l2', C= 1.0, max_iter=100, n_jobs=1, tol=0.0001) LogR = LogR.fit(x_train, y_train) y_pred = LogR.predict(x_test) print(classification_report(y_test, y_pred, digits=3)) precision recall f1-score support 0.0 0.6197 0.9543 0.7514 3413 1.0 0.3305 0.0371 0.0667 2076 avg / total 0.5103 0.6074 0.4924 5489
I would like to make sure, how to interpret the results. In particular, what it implies, practically. Note that I looked into the theory of "precision vs. recall tradeoff", the basic definition of accuracy, precision, and recall, as well as F-score weights.
As I interpret the results,
- Overall accuracy seems like flipping a coin, or, as the averaged F-score suggests, even a bit worse.
- The model has a higher precision in classifying unsuccessful treatments ("0",61%). In particular the 95% recall suggests that it almost does not miss out any of the unsuccessful treatments in the whole test sample.
- However, the model is almost not able to identify successful cases ("1"). It only captures about 33% of the potential candidates and includes many false positives. In addition, the proportion of true positive classifications that are true positive is very, very low (3%).
Assuming you would be a doctor, would these prepositions be correct:
- Intution on high precision, low recall: How many treatments are predicted correctly, at the risk to include a few false positives?
- Intution on low precision, high recall: How many treatments are predicted correctly, at the risk to include a few false negatives? (i.e. how high are the chances to have a prediction for a successful outcomes, whereas truth turns out to be non-successful)
It would be great to have a bit of feedback, or correction of my interpretations.