Hello everybody,
I'm interacting with "Scikit-learn peoples" for the first time, and I
have to say that is an amazing work that you've done here. I am very
grateful for the time you've spent in order that beginners like could
play with such great tools.
Having seen this example
http://scikit-learn.org/dev/auto_examples/plot_precision_recall.html
I've tried to plot the precision / recall curve on my own data.
The result is surprising, in fact most of the curves are consistent,
however, sometimes the output looks like that :
http://s10.postimage.org/d8uazvjt5/pr_curve_1353936568.png
Does it make sense to you ?
I've seen in the bug tracker, a couple of weeks ago, a bug fix on the
_/precision_recall_curve/___function, could It be an explanation ?
Thanks for reading.
All the best,
François.
Ps. here is the code responsible for the aforementioned picture :
def plot_roc(model, X_test, y_test):
probas_ = model.predict_proba(X_test)
fpr, tpr, _thresholds = roc_curve(y_test, probas_[:, 1])
roc_auc = auc(fpr, tpr)
pl.clf()
pl.plot(fpr, tpr, label='ROC curve')
pl.plot([0, 1], [0, 1], 'k--')
pl.xlim([0.0, 1.0])
pl.ylim([0.0, 1.0])
pl.grid()
pl.xlabel('False Positive Rate')
pl.ylabel('True Positive Rate')
pl.title('Receiver operating characteristic (area = %0.2f)' % roc_auc)
pl.legend(loc="best")
pl.savefig(open('./roc_curve_%d.png' % int(time.time()), 'a'),
format="png")
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