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> 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 areafpr["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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