I think it is. The last character of my answer was "_".
That is the variable name to which you assign the thresholds in your code.
On 02/22/2015 01:54 PM, shalu jhanwar wrote:
Hi Andy,
thanks for the reply. I guess your second question's ans. is not
completely displayed in the email. Could you please re-post it?
thanks!
S.
On Sun, Feb 22, 2015 at 10:46 PM, Andy <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
On 02/22/2015 01:21 PM, shalu jhanwar wrote:
Hi guys,
thanks a lot for all your interesting replies.
i) How can I get threshold value which the classifier has decided
to take the decision for a particular sample to be in 0 or 1
class in binary classification using scikit? The whole purpose of
my previous questions were to know about that threshold value
(either by visualising it or just get values)?
That is always 0 for decision_function.
ii) Also can I know the threshold values in each iteration which
scikit used to generate each point on ROC curve using these below
lines:
fpr = dict()
tpr = dict()
roc_auc = dict()
for i in range(n_classes):
fpr[i], tpr[i], _ = roc_curve
<http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve>(y_test[:,
i], y_score[:, i])
roc_auc[i] = auc
<http://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html#sklearn.metrics.auc>(fpr[i],
tpr[i])
# Compute micro-average ROC curve and ROC area
fpr["micro"], tpr["micro"], _ = roc_curve
<http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve>(y_test.ravel(),
y_score.ravel())
roc_auc["micro"] = auc
<http://scikit-learn.org/stable/modules/generated/sklearn.metrics.auc.html#sklearn.metrics.auc>(fpr["micro"],
tpr["micro"])
I'm just using this default code, but I would like to know about
the threshold it uses to generate each point of ROC
The threshold is the third returned value of roc_curve, which you
assign to _
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